WO2022166239A1 - Procédé et appareil de planification de schéma de déplacement de véhicule, et support d'enregistrement - Google Patents

Procédé et appareil de planification de schéma de déplacement de véhicule, et support d'enregistrement Download PDF

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WO2022166239A1
WO2022166239A1 PCT/CN2021/122425 CN2021122425W WO2022166239A1 WO 2022166239 A1 WO2022166239 A1 WO 2022166239A1 CN 2021122425 W CN2021122425 W CN 2021122425W WO 2022166239 A1 WO2022166239 A1 WO 2022166239A1
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road segment
road
traffic
time
segment unit
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PCT/CN2021/122425
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Chinese (zh)
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丁涛
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华为技术有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD

Definitions

  • the present application relates to the technical field of automatic driving, and in particular, to a planning method, device and storage medium for a vehicle driving scheme.
  • Self-driving car is a kind of intelligent car, also known as self-driving car, unmanned car, mainly relying on the control equipment based on computer system in the car to achieve the purpose of driverless. Autonomous vehicles can often sense their surroundings and navigate without human intervention.
  • the control device used to control unmanned driving is used as a vehicle terminal device, sometimes also called electronic control unit (ECU), domain control unit (DCU), or mobile data center (mobile data center, MDC) etc.
  • ECU electronice control unit
  • DCU domain control unit
  • MDC mobile data center
  • the control device can plan a route that can avoid obstacles and conform to the vehicle dynamics, and control the car to drive meticulously according to the planned trajectory. It’s a bit similar to how the brain issues an order for the hand to take something. As for how to take it, the hand itself completes it.
  • the navigation route may be a route with a large span. For example, if a passenger needs to go home from the airport, open the map stored in the on-board terminal, search for "airport" to "home", and there are 35 kilometers, the control device will plan out several routes from the airport to home and display them on the map of the on-board terminal.
  • a route is a navigation route. After the unmanned vehicle determines a navigation route from these routes and automatically drives on the road, it needs to make an evasion according to whether there is a car in front of the current route, and it needs to consider the traffic lights, when to accelerate and when to decelerate.
  • the short-term route is often within the range of 100-200 meters. Since the unmanned vehicle is driving, other vehicles are also driving, so the previously planned short-term route may not always be suitable, and the short-term route will be dynamically adjusted every 0.1 seconds.
  • the embodiments of the present application provide a planning method, device and storage medium for a vehicle driving scheme.
  • an embodiment of the present application provides a method for planning a vehicle driving scheme, the method includes: the method includes: acquiring a navigation route from a starting point to a destination at a specified time, the navigation route includes one or more There are road section units, each of the one or more road section units is a road section between two road points respectively; obtain historical traffic data information of the navigation route; based on the historical traffic data information Or the road traffic model of each road segment unit in the multiple road segment units uses time as the baseline to evaluate the trajectory cost to obtain an evaluation result that meets the planning requirements; according to the evaluation results that meet the planning requirements, determine the two roads for each road segment unit.
  • the travel time and the arrival time corresponding to the points respectively; according to the travel time and the arrival time respectively corresponding to the two waypoints of each road segment unit, the driving plan on the navigation route that meets the planning requirements is determined.
  • the unmanned vehicle when it carries out the driving planning of automatic driving, it can plan based on the historical traffic experience of the current road, and obtain the optimal driving trajectory in the whole process, so that the unmanned vehicle can reach the specific destination within the planned time and obtain the most optimal driving trajectory. Excellent driving experience.
  • the determining, according to the travel time and the arrival time respectively corresponding to the two waypoints in each road segment unit, the driving plan on the navigation route that meets the planning requirement includes: according to the The travel time and arrival time corresponding to the two waypoints in each road segment unit respectively determine the driving speed of the vehicle on each road segment unit; according to the travel time and arrival time corresponding to the two waypoints in each road segment unit respectively The time and the travel speed of the vehicle on each road segment unit are used to determine a travel plan on the navigation route that meets the planning requirements.
  • the historical traffic data information of the navigation route includes one or more of the following:
  • the vehicle control information, travel information, and traffic participant information and traffic information around the vehicle at multiple different times in a historical time period.
  • the historical traffic data information is classified with the time as the baseline and the fixed-length road segment unit as the statistical unit, so as to facilitate the classification statistics and feature extraction of the data with the time as the dimension.
  • the one or more road segment units are obtained by dividing the navigation route according to the length of the road segment, or the one or more road segment units are obtained by dividing the navigation route by traffic flow or traffic elements route obtained.
  • the historical traffic data information of each road section unit is obtained, which is convenient for the classification statistics and feature extraction of the subsequent data.
  • the road section units divided according to traffic flow and traffic elements as the statistical unit, the historical traffic data information of each road section unit is obtained, which is convenient for the classification statistics and feature extraction of the subsequent data.
  • the method further includes establishing a road traffic model of each of the one or more road segment units according to the historical traffic data information.
  • the establishing a road traffic model of each of the one or more road segment units according to the historical traffic data information includes: taking time as a baseline from the historical traffic data information Extract one or more traffic features on each road segment unit from the pass model.
  • the modeling elements can be determined based on the traffic characteristics with the time as the baseline, and the road traffic models at different times with the time as the baseline can be determined based on the modeling elements, thereby ensuring the accuracy and comprehensiveness of the road traffic model.
  • the modeled element values can be determined by selecting several traffic characteristics based on the time as the baseline, and the road traffic models at different times can be determined based on several modeled element values, so as to ensure the accuracy and comprehensiveness of the road traffic model. sex.
  • the method further includes: establishing an evaluation system of the road traffic model according to the historical traffic data information, where the evaluation system of the road traffic model includes the one or more The trajectory cost score corresponding to the traffic feature; the trajectory cost score corresponding to the one or more traffic features at different times is set, and an evaluation system for the road traffic model is established.
  • the traffic characteristics include one or more of the following: vehicle control characteristics, travel characteristics, traffic participant characteristics, traffic characteristics, arrival time characteristics, traffic light waiting time characteristics, and green light travel time characteristics.
  • the trajectory cost evaluation is performed on the road traffic model of each road segment unit based on the historical traffic data information with a time as a baseline, and an evaluation result that meets planning requirements is obtained, including: according to the road
  • the traffic model evaluation system sums the trajectory cost scores corresponding to a plurality of the traffic features of each road traffic model with the time as the baseline or weighted sum, and obtains the traffic model of each road segment unit at different times.
  • Comprehensive trajectory cost value ; compare the comprehensive trajectory cost value with time as a baseline to obtain an evaluation result that meets the planning requirements.
  • the trajectory cost of the road traffic model at different times with the time as the baseline can be evaluated to obtain a road traffic model that meets the planning requirements.
  • the two waypoints of each road segment unit are a start waypoint and an end waypoint, respectively, and the two waypoints of each road segment unit are determined according to the evaluation result that meets the planning requirements.
  • the travel time and arrival time corresponding to the road points respectively include: obtaining the time information corresponding to the road traffic model of each road segment unit according to the evaluation result that meets the planning requirements, and determining the starting waypoint of each road segment unit. Travel time; according to the travel time of the starting waypoint, combined with the road speed limit, specify the arrival time of the ending waypoint of each road segment unit; obtain the travel from the starting waypoint to the ending waypoint time and arrival time.
  • the trajectory cost evaluation can be performed on the road traffic models at different times with the time as the baseline based on the traffic characteristics, so as to obtain a road traffic model that meets the planning requirements.
  • the two waypoints of each road segment unit are a start waypoint and an end waypoint, respectively, and the two waypoints of each road segment unit are determined according to the evaluation result that meets the planning requirements.
  • the travel time and arrival time corresponding to each waypoint including:
  • the time information corresponding to the road traffic model of each road segment unit is obtained, and the travel time of each road segment unit at the starting waypoint is determined;
  • the travel time of the starting waypoint of the unit combined with the road speed limit situation, calculates the interval of the arrival time of the ending waypoint of each road segment unit; according to the interval of the arrival time of the ending waypoint of each road segment unit, Planning the travel time of the next road segment unit of each road segment unit; taking the travel time of the next road segment unit of each road segment unit as the arrival time of each road segment unit, obtain two waypoints for each road segment unit Corresponding travel time and arrival time, respectively.
  • the determining the travel speed of the vehicle on each road segment unit according to the travel time and the arrival time corresponding to the two waypoints in each road segment unit respectively includes: according to the The travel time and arrival time corresponding to the two waypoints of each road section unit respectively, and the speed of the vehicle on the each road section unit is determined by using the speed optimization formula, and the speed optimization formula is:
  • f is the optimization result of the optimization function, and the optimization goal is to minimize f when planning the speed of the unmanned vehicle;
  • si represents the travel of the ith actual waypoint;
  • Re represents the i -th planned waypoint trip;
  • ws represents the position deviation weight;
  • ws represents the acceleration bias weight;
  • Indicates the acceleration of the vehicle, and the subscript i is the planned waypoint of the ith;
  • Indicates the weight of the deviation of the speed value of the vehicle in the acceleration change; represents the speed of the acceleration change, the subscript i ⁇ i+1 is the planned waypoint from the ith to the ith+1;
  • w t represents the arrival time deviation weight;
  • t i represents the travel from the ith waypoint to the destination the estimated time; Represents the planned arrival time when traveling from the i-th waypoint.
  • using the speed optimization formula to determine the driving speed of the vehicle on each road segment unit further comprising:
  • the data information provided by the road traffic model that meets the planning requirements can be used, and the traffic target can be formulated in combination with the current environmental information.
  • the embodiments of the present application provide an apparatus for planning a driving scheme of a vehicle, which may include a module for executing the corresponding module in any of the foregoing embodiments, and the module may be software, hardware, or software and hardware.
  • a module for executing the corresponding module in any of the foregoing embodiments may be software, hardware, or software and hardware.
  • the apparatus may include: a route determination module for acquiring a navigation route from a starting point to a destination at a specified time, the navigation route including one or more road segment units, each road segment in the one or more road segment units
  • the units are respectively a road section between two waypoints;
  • a data acquisition module is used for acquiring historical traffic data information of the navigation route;
  • a model evaluation module is used for the one or more road segments based on the historical traffic data information
  • the road traffic model of each road segment unit in the unit conducts trajectory cost evaluation with time as the baseline, and obtains an evaluation result that meets the planning requirements;
  • the time planning module is used to determine the two units of each road segment unit according to the evaluation result that meets the planning requirements. travel time and arrival time corresponding to each waypoint respectively; and a scheme planning module, configured to determine the travel time and arrival time corresponding to the two waypoints in each road segment unit respectively, which satisfies the planning requirement on the navigation route driving plan.
  • the embodiments of the present application provide an electronic device, and reference may be made to the description in the first aspect for beneficial effects.
  • the electronic device includes a memory and a processor; the processor is configured to execute computer-executed instructions stored in the memory, and the processor executes the computer-executed instructions to execute the unmanned driving plan described in any one of the foregoing embodiments Methods.
  • an embodiment of the present application provides a vehicle, the vehicle including the device described in the second aspect or the third aspect.
  • an embodiment of the present application provides a storage medium, and reference may be made to the description in the first aspect for beneficial effects.
  • the storage medium includes a readable storage medium and a computer program stored in the readable storage medium, where the computer program is used to implement the method for unmanned driving planning described in any one of the foregoing embodiments.
  • a computer program product comprising instructions which, when run on a computer, cause the computer to perform the methods of the above aspects.
  • Figure 1 is a schematic diagram of the navigation route scene from A->B obtained by an unmanned vehicle from a high-precision map in the first scheme
  • FIG. 2 is a flowchart of a method for planning a vehicle driving scheme provided by an embodiment of the present application
  • FIG. 3 is a flowchart of a trajectory cost assessment using time as a baseline for a method for planning a vehicle driving scheme provided by an embodiment of the present application;
  • FIG. 4 is a flowchart of constructing a road traffic model of a method for planning a vehicle driving scheme provided by an embodiment of the present application
  • FIG. 5 is a Time-Cost curve diagram of traveling from a starting point in a method for planning a vehicle driving scheme provided by an embodiment of the present application;
  • FIG. 6 is a scene diagram of traveling from a starting point in a planning method of a vehicle driving scheme provided by an embodiment of the present application, and dividing a fixed route from the starting point to the ending point according to the traffic conditions;
  • FIG. 7 is a functional structural diagram of an apparatus for planning a vehicle driving scheme provided by an embodiment of the present application.
  • FIG. 8 is a schematic diagram of an electronic device for planning a vehicle driving scheme provided by an embodiment of the present application.
  • the high-precision map is an electronic map with high accuracy specially provided for automatic driving, with an accuracy of centimeter level, and usually the accuracy needs to be at the lane line level.
  • Common high-precision map data information includes lane lines, center lines, traffic lights, road signs, stop lines, and lane line types.
  • the planning module of the control device obtains the navigation route from the starting point to the ending point based on the high-precision map. Then, according to the navigation route from the starting point to the ending point, the trajectory waypoint is generated on the high-precision map, and one or more traffic routes are obtained.
  • Each waypoint represents A real road coordinate, which links the waypoints in the point set one by one to form one or more travel routes.
  • the start point, end point, destination and waypoint on the navigation route may also be included in the point set of waypoints.
  • the planning module can perform cost evaluation on the moving trajectory based on various constraints, so that the moving trajectory of the autonomous driving of the unmanned vehicle can be planned according to the cost evaluation result.
  • the constraints may include collision-related constraints, comfort-related constraints, speed-limit-related constraints, and legal compliance-related constraints.
  • the trajectory cost of all constraints on a driving trajectory is accumulated, and the obtained value is the comprehensive cost of the driving trajectory. Sort different driving trajectories according to the value of the comprehensive cost, and select the driving trajectory corresponding to the minimum comprehensive cost as the final short-term optimal driving trajectory according to the sorting.
  • the trajectory cost corresponding to each constraint condition can be set separately.
  • the trajectory cost of collision-related constraints is Cost1.
  • the trajectory cost of speed limit-related constraints is Cost3.
  • the automatic driving planning scheme of the unmanned vehicle has established the full trajectory and the temporary trajectory for automatic driving through the full planning and the local planning, respectively.
  • the unmanned vehicle uses the high-precision map and the current traffic flow information to formulate the entire trajectory from the starting point to the destination point; then, through the sensor data, high-precision map, high-precision positioning and driving trajectory prediction, the current environment of the unmanned vehicle is located. Carry out local planning and formulate the ideal navigation route for the moment.
  • the unmanned vehicle that uses the navigation route planned by this solution to drive automatically even if it is on the same road, the same starting point and ending point, at different times and under different circumstances, the passenger's ride experience and driving effect may be completely different. different.
  • the partially planned driving trajectory of some unmanned vehicles may be to drive fast all the time, but it happens that every intersection will encounter a red light, and it needs to stop and wait for a few seconds, then start slowly and continue driving. Due to frequent acceleration, deceleration, parking and waiting, it brings an extremely poor ride experience to passengers.
  • the partially planned driving trajectory of some unmanned vehicles may be to drive smoothly all the time, and every intersection just encounters a green light, so that there is no need to stop all the way, and the driving experience is excellent.
  • the following introduces a method for planning a vehicle driving scheme provided by an embodiment of the present application.
  • the planning requirement is to make the unmanned vehicle obtain the global optimal or better comprehensive travel effect in one or more of the following aspects as much as possible: the fastest Arrival, minimum start and stop, minimum emergency braking, minimum traffic jam, etc., so that passengers in the car can get a sense of driving experience that meets the planning requirements.
  • the driving experience can be measured according to the average braking acceleration and deceleration, the number of braking accelerations, the turning range, the waiting time, the average speed, the number of avoidance and other indicators.
  • the embodiment of the present application contemplates a planning method for a vehicle driving scheme, which can evaluate the trajectory cost of the road segment at different times on the basis of historical communication data information on each road segment on the navigation route for unmanned vehicles, and according to the evaluation result Obtain the ideal travel time and ideal arrival time of a waypoint on the road segment; obtain a driving plan that meets the planning requirements according to the ideal travel time and ideal arrival time.
  • the ideal arrival time and ideal exit time of a waypoint are at the same time. For example, passengers want to encounter a green light at every intersection, so that they can drive smoothly without stopping all the way.
  • the time period when the green light is on is set as the time constraint of the ideal arrival time and ideal exit time of the waypoint.
  • the speed is solved with time as one of the constraints, and a series of driving schemes that can reach the specific destination on the road section within the specified time are obtained.
  • the historical traffic data information is a collection of data and behavior of vehicles in the history of a road segment. For example, the vehicle control information, itinerary information, and traffic information in the surrounding environment, traffic participant information, etc. of the unmanned vehicle at the historical traffic time on the road section. Based on this information, it is possible to know when to travel, where there will be no traffic jams, and when to travel. You won't encounter a lot of pedestrians, vehicles, etc.
  • the unmanned vehicle can continuously collect the historical traffic data information of the unmanned vehicle itself on the navigation route through the sensors and the domain controller of the vehicle, including vehicle control information, such as accelerator, brake, steering wheel, turn signal, etc., and Environmental information related to itinerary, traffic, traffic participants, etc.
  • vehicle control information such as accelerator, brake, steering wheel, turn signal, etc.
  • Environmental information related to itinerary, traffic, traffic participants, etc.
  • a set of road traffic models based on time is established based on the above historical traffic data information.
  • the modeling elements required to build a road traffic model involve the vehicle control information, itinerary information, traffic information in the surrounding environment, traffic participant information, etc.
  • the vehicle control information of the unmanned vehicle itself includes the number of emergency brakes, the number of emergency avoidance, the number of large accelerators, the number of large brakes, etc.
  • the travel information includes the rate of change of trajectory speed and trajectory curvature, average vehicle speed and arrival time, etc.
  • the surrounding environment The traffic information includes the number of occurrences of blocking obstacles and dangerous obstacles, the number of red lights, the number of green lights and the number of yellow lights, etc.
  • the traffic participant information includes the number of occurrences and waiting times of other passing objects such as vehicles and pedestrians.
  • the emergency braking is a sudden braking phenomenon caused by the sudden braking of the vehicle in order to avoid traffic participants, obstacles, etc. during the driving process.
  • Emergency avoidance is the phenomenon of sharp turning caused by the sudden turning of the steering wheel in order to avoid traffic participants, obstacles, etc. during the driving process.
  • the blocking obstacle is the obstacle that causes the unmanned vehicle to brake and stop, and is called the blocking obstacle of the unmanned vehicle.
  • Dangerous obstacles are obstacles that are too close to the unmanned vehicle, or the trajectory intersects with the unmanned vehicle, and may cause the unmanned vehicle to take emergency takeover, braking, and evasion.
  • the road traffic model describes the traffic information of a road segment at different times in a time period
  • the traffic information may include one or more traffic characteristics of the current road segment, and the traffic characteristics may be expressed as time changes Therefore, the evaluation score of the trajectory cost corresponding to the traffic feature varies with time.
  • the next waypoint can be used as the destination, and the road traffic model of the next road segment can be used to evaluate the trajectory cost at different times, and calculate the ideal travel time and ideal arrival time of the next waypoint.
  • Plan the driving plan of the unmanned vehicle. This cycle continues until the end point, and a driving plan that meets the planning requirements throughout the navigation route is obtained.
  • An unmanned vehicle that uses the travel plan planned by the evaluation result of the road traffic model can reach a specific destination within the planned time and obtain a better driving experience.
  • control device of the unmanned vehicle Before executing the planning method of the vehicle driving scheme provided by the embodiment of the present application, the control device of the unmanned vehicle also establishes a road traffic model and an evaluation system of the road traffic model according to historical traffic data information.
  • FIG. 2 is a flowchart of a method for planning a vehicle driving scheme provided by an embodiment of the present application. As shown in FIG. 2 , the control device of the unmanned vehicle performs the following steps to plan the driving scheme:
  • S21 Acquire a navigation route from a starting point to a destination at a specified time, where the navigation route includes one or more road segment units, and each road segment unit is a road segment between two waypoints on the navigation route.
  • control device of the unmanned vehicle can obtain the navigation route from the starting point to the destination at the current moment from the high-precision map, and divide the navigation route into multiple road segments.
  • the road segment between every two waypoints is recorded as a road segment unit.
  • the navigation route from the starting point to the destination can be divided into multiple road segments according to the length of the road segment.
  • the two waypoints are the starting point and the end point of the two ends of the road segment.
  • the 200-meter section length divides the navigation route.
  • the navigation route from the starting point to the destination may be divided into a plurality of road segments according to traffic flow or traffic elements.
  • the navigation route can be divided into multiple road segments according to the waypoints before and after the traffic light intersection.
  • the two waypoints can be the locations of the traffic light intersections at both ends of the road segment, or according to the waypoints before and after the road segment where traffic congestion often occurs.
  • the waypoint divides the navigation route into multiple sections. Accordingly, the two waypoints can be the starting point and the end point at both ends of the congested section; or the navigation route is divided into multiple sections according to the waypoints before and after the intersection.
  • the two waypoints can be where the intersections at both ends of the road segment are located.
  • control device of the unmanned vehicle collects historical traffic data information based on time on each road segment of the current navigation route.
  • the historical traffic data information at a certain moment on each road segment may include the vehicle control information and travel information of the unmanned vehicle passing on the road segment; it may include the traffic information of other vehicles participating in the traffic, for example, Driving speed, quantity, etc., the traffic information of other vehicles is recorded as the traffic participant information, and it can also include the traffic information of the road section at the moment, for example, the duration of traffic lights, the duration of congestion, etc.
  • the vehicle control information and itinerary information at a certain time can be collected through the sensors of the unmanned vehicle and the vehicle domain controller; or all traffic at a certain time can be obtained through the intelligent network connection or through the data stored in the cloud Participants' information and traffic information at a certain time.
  • the historical traffic data information based on time includes the historical traffic data information of multiple moments within a certain set period of time.
  • S23 based on the historical traffic data information, perform a trajectory cost evaluation on the road traffic model of each road section with time as a baseline, and obtain an evaluation result that meets the planning requirements. As shown in FIG. 3 , S23 is specifically implemented by executing the following steps S231-S233.
  • the road traffic model of the unmanned vehicle is determined as:
  • Key i ⁇ j is the road traffic model of the i-th road segment to the j-th road segment
  • Key n is the road traffic model of the n-th road segment
  • key nm is the m-th traffic feature on the n-th road segment.
  • i, j and m are natural numbers.
  • the evaluation formula corresponding to the road traffic model of the unmanned vehicle in formula (1) is:
  • Cost i ⁇ j is the comprehensive trajectory cost of the navigation route from the i-th road segment to the j-th road segment
  • Cost i is the trajectory cost of the road segment starting from the i-th waypoint
  • W 1 represents the weight value of the traffic feature key n1
  • cost nt1 represents the trajectory cost of the traffic feature key n1 of the n-th road segment at time t
  • W 2 represents the weight value of the traffic feature key n2
  • cost it2 represents the traffic of the n-th road segment at time t
  • W m represents the weight value of the passing feature key nm
  • cost ntm represents the trajectory cost of the passing feature key nm of the nth road section at time t.
  • Formula (2) shows that the comprehensive trajectory cost of the navigation route from the ith waypoint to the jth waypoint is the accumulation of the trajectory cost of the road traffic model corresponding to the ith road segment to the jth road segment;
  • the value of the comprehensive trajectory cost between the road segment and the jth road segment is also the value of the weighted summation of the trajectory cost scores corresponding to all the traffic features on the route.
  • the vehicle control characteristics, travel characteristics, traffic participant characteristics and the trajectory cost scores corresponding to the traffic characteristics at different times on the current road segment are calculated as The time as the baseline is weighted and summed to obtain the comprehensive trajectory cost of the road traffic model of each road segment with the time as the baseline.
  • the statistical threshold corresponding to the arrival time feature, the statistical threshold corresponding to the traffic light waiting time feature, and the statistical threshold corresponding to the green light passing time feature can be obtained according to historical traffic data statistics; according to the road traffic model evaluation system, Taking time t as the baseline, the trajectory cost scores corresponding to the statistical thresholds of the arrival time characteristics, traffic light waiting time characteristics and green light passing time characteristics of each road segment at time t are weighted and summed to obtain the comprehensive trajectory cost of the road traffic model.
  • S233 Evaluate the comprehensive trajectory cost value at different times on the current road section with the time as the baseline, and obtain an evaluation result that meets the planning requirements.
  • the planning requirement is to make the unmanned vehicle obtain the overall optimal or better comprehensive travel effect in one or more of the following aspects as far as possible: the fastest arrival, the least start and stop, the least sudden braking, the least traffic jam, etc.
  • a driving experience that meets the planning requirements can be obtained.
  • Whether it meets the planning requirements can be assessed by the score of the comprehensive trajectory cost of the road traffic model.
  • the comprehensive trajectory cost values of the road traffic models on the current road section at different times can be compared, and the time t corresponding to the larger comprehensive trajectory cost value is used as the evaluation result that meets the planning requirements.
  • the trajectory cost score of each traffic feature is set based on the principle that the larger the trajectory cost score is, the more it meets the planning requirements, then the time t corresponding to the larger comprehensive trajectory cost value is the one that meets the planning requirements. Evaluation results of planning requirements.
  • the comprehensive trajectory cost values of the road traffic models on the current road section at different times can be compared, and the time t corresponding to the smaller comprehensive trajectory cost value is used as the evaluation result that meets the planning requirements.
  • the trajectory cost score of each traffic feature is set based on the principle that the smaller the trajectory cost score, the more in line with the planning requirements, then the time t corresponding to the smaller comprehensive trajectory cost value is consistent with the planning requirements. Evaluation results of planning requirements.
  • control device of the unmanned vehicle combines the current location and the current travel time, and selects the time corresponding to the evaluation result that meets the planning requirements as the ideal travel time for the current road point.
  • the ideal arrival time to the next waypoint is obtained by planning.
  • the time information corresponding to the road traffic model can be obtained according to the evaluation results that meet the planning requirements, and the travel time of the starting waypoint of each road segment can be determined; according to the starting waypoint of each road segment According to the speed limit of the road, specify the arrival time of the end waypoint; obtain the travel time and arrival time from the start waypoint to the end waypoint.
  • the time information corresponding to the road traffic model is obtained according to the evaluation result that meets the planning requirements, and the travel time of the starting waypoint of each road section is determined;
  • the travel time of the road point combined with the road speed limit, calculate the interval of the arrival time of the terminal road point of each road segment; according to the interval of the arrival time of the terminal road point, take the terminal road point of the next road segment of each road segment
  • the corresponding speed of the trajectory waypoint is planned according to the travel time of the current waypoint and the ideal arrival time of the next waypoint.
  • the ideal arrival time is used as an input parameter and input into the speed optimization formula.
  • a reasonable corresponding speed of the trajectory waypoint is planned to achieve the goal of reaching the target location at the ideal arrival time.
  • the speed optimization formula is:
  • f is the optimization result of the optimization function, and the optimization goal is to minimize f when planning the speed of the unmanned vehicle;
  • si represents the travel of the ith actual waypoint;
  • Re represents the i -th planned waypoint trip;
  • ws represents the position deviation weight;
  • ws represents the acceleration bias weight;
  • Indicates the acceleration of the vehicle, and the subscript i is the planned waypoint of the ith;
  • Indicates the weight of the deviation of the speed value of the vehicle in the acceleration change; represents the speed of the acceleration change, the subscript i ⁇ i+1 is the planned waypoint from the ith to the ith+1;
  • w t represents the arrival time deviation weight;
  • t i represents the travel from the ith waypoint to the destination the estimated time; Represents the planned arrival time when traveling from the i-th waypoint.
  • constraints for setting formula (3) include:
  • ⁇ t is the difference between the travel time and the arrival time planned from the ith waypoint
  • S252 Plan on the navigation route according to the travel time, arrival time and travel speed of each road segment unit, and obtain a travel plan that meets the planning requirements.
  • the control device of the unmanned vehicle determines whether the end point is reached, and if the determination result is "No", the trajectory planning of the next road segment is performed, and S24 is executed. If the judgment result is "Yes”, plan the navigation route according to the travel time, arrival time and driving speed of each road section, obtain a driving plan that meets the planning requirements, and complete the driving trajectory planning of this unmanned vehicle.
  • the method for planning the driving trajectory of an unmanned vehicle is based on the road traffic model of the unmanned vehicle, optimizes the travel time of the navigation route, and uses the time as a constraint condition to participate in the planning of the driving trajectory and the driving speed, so as to achieve the position-specific , speed and time triple constraints and planning.
  • This method makes use of current and historical traffic experience to make the trajectory planning of the unmanned vehicle globally optimal, and the driving experience and traffic efficiency are greatly improved.
  • a road traffic model is also established according to the historical traffic data information.
  • the control device of the unmanned vehicle selects a modeling formula to establish a series of time baseline road traffic models for the current road segment.
  • a modeling formula to establish a series of time baseline road traffic models for the current road segment.
  • Key all is the road traffic model of the planned route
  • key n is the road traffic model of the nth road segment
  • length is the number of road segments obtained by segmenting the entire road.
  • a number of different traffic features can be extracted for modeling through historical traffic data information.
  • the corresponding vehicle control features and itinerary features can be extracted by collecting the historical traffic data information of the vehicle itself on a certain planned route, or the traffic participant features and traffic features can be extracted from the data stored in the intelligent network connection and the cloud. .
  • establishing a road traffic model according to the historical traffic data information includes the following steps:
  • the traffic features on each road section unit are extracted from the data information of historical traffic experience based on time; traffic features include vehicle control features, travel features, traffic participant features, traffic features, arrival time features, traffic lights Waiting time characteristics and green light transit time characteristics.
  • S42 Accumulate one or more traffic features on each road segment unit to establish multiple road traffic models on each road segment with time as a baseline.
  • the baseline select one or more traffic features from the vehicle control features, travel features, traffic participant features, traffic features, arrival time features, traffic light waiting time features, and green light transit time features on each road segment unit.
  • the features are used as modeling elements to accumulate traffic features to generate a time-based road traffic model.
  • the road traffic model can be obtained by modeling using recurrent neural networks (RNN).
  • RNN recurrent neural networks
  • a method for planning a vehicle driving scheme wherein the road traffic model adopts a plurality of different traffic feature modeling, and the data corresponding to the plurality of different traffic features covers comprehensive historical traffic data information, which can Ensure the accuracy of the road traffic model; evaluate and plan the driving plan according to the accurate road traffic model, so that the unmanned vehicle can achieve a good traffic effect when driving, so that the passengers traveling by the unmanned vehicle can obtain a satisfactory driving experience.
  • an evaluation system of the road traffic model is also established according to the historical traffic data information.
  • a road traffic model evaluation system can be established by formulating trajectory cost scoring criteria for traffic characteristics and thresholds. According to a number of different traffic characteristics and thresholds, the trajectory cost scoring criteria for traffic characteristics and thresholds are formulated.
  • the traffic characteristics required by the road traffic model of the road section and the statistical threshold corresponding to the traffic characteristics are selected.
  • a modeling formula is selected to establish a series of time baseline road traffic models with the current road point as the starting point.
  • a modeling formula is selected to establish a series of time baseline road traffic models with the current road point as the starting point.
  • Cost all is the comprehensive trajectory cost of the planned route
  • Cost n is the trajectory cost of the nth road segment
  • length is the number of road segments obtained by segmenting the entire road.
  • one or more traffic features can be extracted from historical traffic data information; the trajectory cost score corresponding to one or more traffic features is set with time as the baseline, and an evaluation system of the road traffic model is established .
  • the trajectory cost score of each traffic feature is set based on the principle that the smaller the trajectory cost score, the more in line with the planning requirements. Or the larger the trajectory cost score is, the more in line with the planning requirements, the trajectory cost score of each traffic feature is set based on the principle.
  • traffic features can be extracted from historical traffic data information, and the traffic features include vehicle control features, travel features, traffic participant features and traffic features, arrival time features, and traffic light waiting time features of unmanned vehicles. and green light transit time characteristics. Based on the time as the baseline, set the vehicle control characteristics, travel characteristics, traffic participant characteristics and traffic characteristics, arrival time characteristics, traffic light waiting time characteristics and green light passing time characteristics corresponding to the trajectory cost scores at different times, and establish the evaluation of the road traffic model. system.
  • Threshold 3 7min. In the planning of the trajectory cost corresponding to the traffic feature, 2 points are added for exceeding the threshold3, and less than the threshold3 is not added. point.
  • the embodiments of the present application provide a planning method for a vehicle driving scheme, make a road traffic model of the unmanned vehicle, and plan the driving trajectory of the unmanned vehicle. Specific steps are as follows:
  • a relevant modeling element as a traffic feature (key) of a road traffic model of the unmanned vehicle, set a corresponding evaluation score (Value) of a trajectory cost (cost) for the traffic feature, and set a value for some specific building blocks
  • the pass characteristic of the modulo element sets the relevant threshold (threshold).
  • the modeling elements involved in the modeling of the road traffic model of unmanned vehicles include vehicle control characteristics and environmental characteristics.
  • the traffic features of the vehicle control features in Table 1 include "emergency avoidance", and the corresponding evaluation score of the trajectory cost is "+5"; during local planning, if the unmanned vehicle is currently known according to the historical traffic data information If an emergency avoidance has occurred at the roadpoint once, the trajectory cost of the road traffic model of the current road section will be increased by 5 points. If the unmanned vehicle makes multiple emergency avoidance at the roadpoint, the trajectory cost of the road traffic model of the road section will be increased by 5 points. *m points, m is the number of times of emergency avoidance.
  • the traffic features of the vehicle control features in Table 1 include the "arrival time” feature, and the corresponding evaluation score is ">threshold+2"; during local planning, if the unmanned vehicle is calculated according to the historical traffic data information and the planned speed information
  • 2 points are added to the trajectory cost cost of the road traffic model of the current road segment. For example, set the threshold of arrival time to 2 minutes. If the time for the unmanned vehicle to reach the current target waypoint at the current speed exceeds the specified arrival time by more than 2 minutes, the trajectory cost of the current road traffic model will add 2 points. , no additional points are added for trajectory costs below the threshold.
  • the traffic features of the environmental information in Table 1 include the "red light” feature, and its corresponding evaluation score is "+3"; in local planning, if the unmanned vehicle encounters a red light before reaching the target waypoint, then The trajectory cost of the road traffic model adds 3 points. If the unmanned vehicle encounters multiple red lights before reaching the target waypoint, the cost of the trajectory cost of the road traffic model of this road segment is added by 3*m points, where m is the number of red lights.
  • Table 1 also shows the traffic characteristics of other vehicle control information and environmental information and their evaluation scores.
  • the evaluation scores of the specific trajectory cost are similar to the above examples, and will not be listed one by one.
  • a simple road traffic model of the unmanned vehicle is established.
  • the road traffic model is modeled by using the traffic time feature and the waiting time feature as the modeling element values, and the current road traffic model of the unmanned vehicle is evaluated as:
  • Cost i ⁇ j is the comprehensive trajectory cost of the planned route from the i-th road segment to the j-th road segment
  • Cost n is the trajectory cost corresponding to the n-th road segment.
  • W1 represents the weight value of the transit time feature
  • Cost nt1 represents the trajectory cost of the transit time of the nth road segment at time t
  • W2 represents the weight value of the waiting time feature
  • Cost nt2 represents the trajectory corresponding to the waiting time feature of the nth road segment at time t Cost
  • wait time features include red light wait time and/or congestion wait time.
  • Formula (9) shows the road traffic model evaluation formula of the route between the i-th road point to the j-th road point, which is the accumulation of the trajectory cost of the road traffic model of each road segment corresponding to the i-th road segment to the j-th road segment.
  • the value of the comprehensive trajectory cost of the route between the i-th road segment to the j-th road segment is the accumulation of the trajectory cost of each road segment corresponding to the i-th road segment to the j-th road segment;
  • the i-th road segment to the j-th road segment The value of the comprehensive trajectory cost of the route between them is also the value of the weighted summation of the trajectory cost scores corresponding to the travel time feature and the waiting time feature on the planned route.
  • FIG. 5 is a Time-Cost curve diagram of traveling from the starting point of a certain planned route. As shown in FIG. 5 , the horizontal axis is the time axis (Time), and the vertical axis is the trajectory cost axis (Cost).
  • the trajectory cost Cost i ⁇ j of the road traffic model is calculated as 15; when the travel time is 8:00, the trajectory cost Cost i ⁇ j of the road traffic model is calculated as 20; when the travel time is At 8:10, calculate the trajectory cost of the road traffic model Cost i ⁇ j is 32; when the travel time is 8:20, calculate the trajectory cost of the road traffic model Cost i ⁇ j is 46; when the travel time is 8:30 , Calculate the trajectory cost of the road traffic model Cost i ⁇ j is 35; when the travel time is 8:40, calculate the trajectory cost of the road traffic model Cost i ⁇ j is 20; when the travel time is 8:50, calculate the road traffic The trajectory cost Cost i ⁇ j of the model is 18.
  • the unmanned vehicle user starts to travel from the starting point at 7:50, and the fixed route from the starting point to the ending point is divided into three sections according to the traffic conditions.
  • the three road segments are respectively the starting point->D1 of waypoint 1, D2 of waypoint 1->waypoint 2, and D3 of waypoint 2->end point.
  • waypoint 1 is before the traffic light intersection
  • D1 represents the first road segment from the starting point to the traffic light intersection
  • waypoint 2 is after the traffic light intersection
  • D2 represents the second road segment from the traffic light intersection to the back of the traffic light intersection
  • D3 represents from the traffic light intersection The third section after the intersection to the end.
  • S505 take 7:50 as the starting point for travel time, calculate according to the speed limit of the road section and the trajectory planning of the unmanned vehicle, and obtain the time range of arriving at road point 1 in the range of 8:10-8:30, and in the time range of 8:10-8:30.
  • the trajectory cost Cost 1 of D1 is calculated according to the road traffic model of the unmanned vehicle, and the ideal travel time point of road point 1 is 8:20.
  • the time 8:20 is used as the constraint condition of speed trajectory optimization, and is brought into formulas (3)-(5) to solve the speed, and a path starting at 7:50 and arriving at waypoint 1 at 8:20 meets the planning requirements driving plan.
  • the unmanned vehicle it is judged whether the unmanned vehicle has reached the end point, and if the judgment result is "No", take 8:20 as the travel time of waypoint 1, and calculate according to the road speed limit and the trajectory planning of the unmanned vehicle, and obtain the arrival time of waypoint 2.
  • the time range is 8:25-8:35.
  • the trajectory cost Cost 2 of the D2 road section is calculated.
  • the travel time point corresponding to the ideal road point 2 is obtained as At 8:30, it can drive smoothly without stopping; take the arrival time of 8:20 as the constraint condition of speed trajectory optimization, bring it into formulas (3)-(5) to solve the speed, and get the arrival time at 8:30. 2.
  • the driving scheme that meets the planning requirements.
  • the above-mentioned embodiments plan the driving scheme according to the road traffic model based on the unmanned vehicle.
  • the model data can be fully utilized to avoid the waiting time of the red light and the waiting of the congested road section, so as to improve the traffic efficiency and improve the traffic efficiency. driving experience.
  • Embodiments of the present application also provide a device for planning a vehicle driving scheme, the device can be deployed or integrated on a vehicle, is a part of an on-board system, and can be an on-board control unit, such as an ECU, DCU, or MDC, etc., or a Semiconductor chips installed in in-vehicle systems, etc.
  • an on-board control unit such as an ECU, DCU, or MDC, etc.
  • Semiconductor chips installed in in-vehicle systems, etc.
  • the device obtains the navigation route from the starting point to the destination at the specified time through the route determination module 71, the navigation route includes one or more road segment units, and each road segment unit in the one or more road segment units is two respectively The road section between the waypoints;
  • the historical traffic data information of the navigation route is acquired by the data acquisition module 72;
  • the road traffic model of each road section unit in the one or more road section units is determined by the model evaluation module 73 based on the historical traffic data information with time as The trajectory cost evaluation is performed on the baseline to obtain an evaluation result that meets the planning requirements;
  • the time planning module 74 determines the travel time and arrival time corresponding to the two waypoints of each road segment unit according to the evaluation results that meet the planning requirements;
  • the solution planning module 75 According to the travel time and arrival time respectively corresponding to the two waypoints in each road segment unit, the driving plan on the navigation route that meets the planning requirements is determined.
  • the time planning module includes: a speed calculation unit and a scheme planning unit; the device uses the speed calculation unit according to the travel times corresponding to the two road points of each road section unit respectively and the arrival time to determine the speed of the vehicle on each road segment unit; the solution planning unit determines the navigation based on the travel time and arrival time corresponding to the two waypoints in each road segment unit and the vehicle's driving speed on each road segment unit. The driving plan on the route that meets the planning requirements.
  • the historical traffic data information of the navigation route includes one or more of the following: the vehicle is on one or more road segment units, and the vehicle control information, itinerary information, and vehicle traffic at multiple different times in a historical time period Information about surrounding traffic participants and traffic information.
  • one or more road segment units are obtained by dividing the navigation route according to the length of the road segment, or one or more road segment units are obtained by dividing the navigation route according to traffic flow or traffic elements route obtained.
  • a model building module is further included, and a road traffic model of each road segment unit in one or more road segment units is established by the model building module according to the historical traffic data information.
  • building a model module includes using a feature extraction unit to extract one or more traffic features on each road section unit from historical traffic data information with time as a baseline; or multiple traffic features, and establish multiple road traffic models based on time on each road segment unit.
  • the modeling subunit accumulates one or more traffic features on each road segment unit to establish multiple time-based road traffic models on each road segment unit.
  • the modeling subunit is configured to use one or more traffic features on each road segment unit to establish a time-based road traffic model through an RNN recurrent neural network.
  • the apparatus further includes an evaluation system establishment module, and the evaluation system establishment module establishes an evaluation system of a road traffic model according to the historical traffic data information.
  • the evaluation system includes trajectory cost scores corresponding to one or more traffic features at different times; set the trajectory cost scores corresponding to one or more traffic features at different times to establish an evaluation system for the road traffic model.
  • the traffic characteristics include one or more of the following: vehicle control characteristics, travel characteristics, traffic participant characteristics, traffic characteristics, arrival time characteristics, traffic light waiting time characteristics, and green light passing time characteristics.
  • the model evaluation module includes a calculation unit and an evaluation unit; the device uses the calculation unit to calculate the trajectory cost score corresponding to a plurality of the traffic features of each road traffic model according to the road traffic model evaluation system as The time is the baseline summation or weighted summation to obtain the comprehensive trajectory cost value of each road traffic model of each road segment unit at different times; and the evaluation unit is used to compare the comprehensive trajectory cost value with the time as the baseline to obtain Evaluation results that meet planning requirements.
  • a transit time planning module of the apparatus obtains time information corresponding to a road passing model of each road segment unit according to an evaluation result that meets the planning requirements, and determines each road segment The travel time of the starting waypoint of the unit; according to the traveling time of the starting waypoint of each road segment unit, combined with the road speed limit, specify the arrival time of the ending waypoint of each road segment unit; obtain from the starting waypoint to the ending waypoint travel time and arrival time.
  • the device obtains the time information corresponding to the road traffic model of each road segment unit through the time planning module according to the evaluation result that meets the planning requirements, and determines the travel time of each road segment unit at the starting waypoint;
  • the travel time of the starting waypoint of the road segment unit combined with the road speed limit, calculates the interval of the arrival time of the ending waypoint of each road segment unit;
  • the travel time of the next road segment unit of each road segment unit taking the travel time of the next road segment unit of each road segment unit as the arrival time of each road segment unit, obtain the corresponding road points of each road segment unit respectively Travel time and arrival time.
  • the speed calculation unit is used for:
  • the speed optimization formula is used to determine the driving speed of the vehicle on each road segment unit.
  • the speed optimization formula is as formula (3):
  • f is the optimization result of the optimization function, and the optimization goal is to minimize f when planning the speed of the unmanned vehicle;
  • si represents the travel of the ith actual waypoint;
  • Re represents the i -th planned waypoint trip;
  • ws represents the position deviation weight;
  • ws represents the acceleration bias weight;
  • Indicates the acceleration of the vehicle, and the subscript i is the planned waypoint of the ith;
  • Indicates the weight of the deviation of the speed value of the vehicle in the acceleration change; represents the speed of the acceleration change, the subscript i ⁇ i+1 is the planned waypoint from the ith to the ith+1;
  • w t represents the arrival time deviation weight;
  • t i represents the travel from the ith waypoint to the destination the estimated time; Represents the planned arrival time when traveling from the i-th waypoint.
  • the velocity calculation unit is also used to:
  • ⁇ t is the difference between the travel time and the arrival time planned from the ith waypoint
  • an embodiment of the present application provides an electronic device 1100, including a processor 1101 and a memory 1102; the processor 1101 is configured to execute computer-executed instructions stored in the memory 1102, and the processor 1101 runs The computer executes the instructions to execute the method for unmanned driving planning described in any of the foregoing embodiments.
  • This embodiment of the present application provides a storage medium 1103, including a readable storage medium and a computer program stored in the readable storage medium, where the computer program is used to implement the unmanned driving planning described in any of the foregoing embodiments. method.
  • Embodiments of the present application further provide a vehicle, where at least one device for planning a vehicle driving scheme is deployed or integrated in the vehicle, and the device is a part of an in-vehicle system, which may be an in-vehicle control unit, such as an ECU, DCU, or MDC, etc., It may also be a semiconductor chip or the like provided in an in-vehicle system.
  • an in-vehicle control unit such as an ECU, DCU, or MDC, etc.
  • the vehicle can travel according to the driving plan planned by the device according to the method of any one of the above embodiments;
  • the device includes at least: a route determination module, configured to obtain a navigation route from a starting point to a destination at a specified time, the navigation route including one or A plurality of road section units, each of which is a road section between two road points in one or more road section units; a data acquisition module for acquiring historical traffic data information of a navigation route; a model evaluation module for historically based
  • the traffic data information is used to evaluate the trajectory cost of the road traffic model of each road segment unit in one or more road segment units with time as the baseline, and obtain the evaluation results that meet the planning requirements;
  • the time planning module is used to determine the evaluation results that meet the planning requirements.
  • various aspects or features of the embodiments of the present application may be implemented as a method, apparatus or article of manufacture using standard programming and/or engineering techniques.
  • article of manufacture encompasses a computer program accessible from any computer-readable device, carrier or medium.
  • computer readable media may include, but are not limited to: magnetic storage devices (eg, hard disks, floppy disks, or magnetic tapes, etc.), optical disks (eg, compact discs (CDs), digital versatile discs (DVDs) etc.), smart cards and flash memory devices (eg, erasable programmable read-only memory (EPROM), card, stick or key drives, etc.).
  • various storage media described herein can represent one or more devices and/or other machine-readable media for storing information.
  • the term "machine-readable medium” may include, but is not limited to, wireless channels and various other media capable of storing, containing, and/or carrying instructions and/or data.
  • the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be The implementation process of the embodiments of the present application constitutes any limitation.
  • the disclosed system, apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • the functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium.
  • the technical solutions of the embodiments of the present application can be embodied in the form of software products in essence, or the parts that make contributions to the prior art or the parts of the technical solutions, and the computer software products are stored in a storage medium , including several instructions to cause a computer device (which may be a personal computer, a server, or an access network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the embodiments of this application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

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Abstract

Procédé et appareil de planification de schéma de déplacement de véhicule, et support d'enregistrement. Le procédé comprend : l'acquisition d'un itinéraire de navigation d'un point de départ à une destination à un moment désigné, l'itinéraire de navigation comprenant une ou plusieurs unités de segment de route, et chacune de la ou des unités de segment de route étant un segment de route entre deux points de cheminement ; l'acquisition d'informations de données de circulation historiques de l'itinéraire de navigation ; sur la base des informations de données de circulation historiques, l'évaluation du coût de trajectoire d'un modèle de circulation routière de chacune de la ou des unités de segment de route en prenant le temps en tant que ligne de base, de façon à obtenir un résultat d'évaluation qui satisfait une exigence de planification ; en fonction du résultat d'évaluation qui répond à une exigence de planification, la détermination d'un temps de départ et d'un temps d'arrivée correspondant respectivement aux deux points de cheminement de chaque unité de segment de route ; et en fonction du temps de départ et du temps d'arrivée correspondant respectivement aux deux points de cheminement de chaque unité de segment de route, la détermination d'un schéma de déplacement, qui satisfait l'exigence de planification, sur l'itinéraire de navigation.
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