FR3130433A1 - Process for the real-time predictive analysis of the occurrence of an accident in an accident-prone area - Google Patents
Process for the real-time predictive analysis of the occurrence of an accident in an accident-prone area Download PDFInfo
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- FR3130433A1 FR3130433A1 FR2113254A FR2113254A FR3130433A1 FR 3130433 A1 FR3130433 A1 FR 3130433A1 FR 2113254 A FR2113254 A FR 2113254A FR 2113254 A FR2113254 A FR 2113254A FR 3130433 A1 FR3130433 A1 FR 3130433A1
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- 238000004458 analytical method Methods 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 title claims abstract description 12
- 238000013528 artificial neural network Methods 0.000 claims abstract description 7
- 230000005540 biological transmission Effects 0.000 claims abstract description 6
- 238000004891 communication Methods 0.000 claims description 6
- 206010039203 Road traffic accident Diseases 0.000 claims description 2
- 230000004044 response Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 230000009931 harmful effect Effects 0.000 description 2
- 102100034112 Alkyldihydroxyacetonephosphate synthase, peroxisomal Human genes 0.000 description 1
- 101000799143 Homo sapiens Alkyldihydroxyacetonephosphate synthase, peroxisomal Proteins 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000000848 angular dependent Auger electron spectroscopy Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/164—Centralised systems, e.g. external to vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096716—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information does not generate an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096783—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a roadside individual element
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
Abstract
L’invention concerne un procédé d’analyse prédictive en temps réel par un dispositif débarqué (10) de la survenue d’un accident sur une zone de circulation routière accidentogène (1), caractérisé en ce qu’il comprend les étapes successives, d’émission de requêtes de transmission des données de conduite à l’ensemble des véhicules présents (5, 6, 7, 8) dans ladite zone accidentogène, de réception desdites données de conduite en provenance desdits véhicules, de détermination du contexte de conduite courant dans ladite zone accidentogène, d’estimation via un algorithme utilisant un réseau neuronal préalablement entrainé à partir d’une base d’apprentissage du caractère à risque élevé d’accident ou non dudit contexte de conduite courant dans ladite zone accidentogène, et de transmission à chaque véhicule présent dans ladite zone accidentogène d’un ordre d’émission d’une alerte à l’intention du conducteur de ce véhicule lorsque ledit contexte de conduite courant a été identifié comme à risque élevé d’accident. Figure à publier avec l’abrégé : Fig. 1The invention relates to a method for predictive analysis in real time by an on-board device (10) of the occurrence of an accident in an accident-prone road traffic area (1), characterized in that it comprises the successive steps, issuing requests for transmission of driving data to all the vehicles present (5, 6, 7, 8) in said accident-prone zone, for receiving said driving data from said vehicles, for determining the current driving context in said accident-prone zone, of estimation via an algorithm using a previously trained neural network from a learning base of the character at high risk of accident or not of said current driving context in said accident-prone zone, and of transmission to each vehicle present in said accident-prone zone of an order to send an alert to the driver of this vehicle when said current driving context has been identified as at high risk of accident. Figure to be published with abstract: Fig. 1
Description
La présente invention concerne d’une manière générale la problématique de la sécurité dans le domaine automobile. Elle vise en particulier un procédé d’analyse prédictive en temps réel de la survenue d’un accident sur une zone de circulation routière accidentogène.The present invention generally relates to the problem of safety in the automotive field. It aims in particular at a method of predictive analysis in real time of the occurrence of an accident in an accident-prone road traffic area.
L’augmentation continue du nombre de véhicules automobiles en circulation a pour conséquence d’entrainer un accroissement du nombre d’accidents de la route qui, malgré l’amélioration constante des infrastructures routières, demeurent l’une des principales causes de mortalité eu Europe et dans le reste du monde.The continuous increase in the number of motor vehicles in circulation has resulted in an increase in the number of road accidents which, despite the constant improvement of road infrastructures, remain one of the main causes of death in Europe and in the rest of the world.
Par ailleurs, les accidents de la route ont également un impact socio-économique très important, du fait des blessures physiques occasionnées et leurs conséquences sur la vie des personnes touchées.In addition, road accidents also have a very significant socio-economic impact, due to the physical injuries caused and their consequences on the lives of those affected.
Afin de limiter les risques d’accidents, les constructeurs et équipementiers automobiles ont développés et mis en œuvre depuis une quinzaine d’années des systèmes d’aides à la conduite sophistiqués, communément désignés sous l’acronyme anglophone ADAS pour « Advanced Driver Assistant Systems ».In order to limit the risk of accidents, automobile manufacturers and suppliers have been developing and implementing sophisticated driver assistance systems for fifteen years, commonly referred to by the English acronym ADAS for "Advanced Driver Assistant Systems ".
Pour réaliser une détection d'obstacles depuis un véhicule automobile, de nombreuses solutions ont été envisagées et utilisées, essentiellement basées sur des technologies radar, lidar, ultrasons pour les obstacles proches et à basse vitesse, et également sur des procédés faisant intervenir des dispositifs de type caméras.To carry out obstacle detection from a motor vehicle, many solutions have been considered and used, essentially based on radar, lidar, ultrasound technologies for close obstacles and at low speed, and also on methods involving detection devices. type cameras.
Les capteurs de type radar et lidar, typiquement utilisés en application à détection lointaine comme l'ACC (pour « Autonomous Cruise Control » en langue anglaise) par exemple, fournissent directement une information de position et parfois de vitesse 3D des objets présents dans la scène. Cette information permet de segmenter les différents objets présents et de leur attribuer des informations utiles comme leur position, éventuellement leur vitesse, avec leurs coordonnées réelles en trois dimensions, par rapport au véhicule équipé du capteur considéré, et par rapport à un repère fixe, si la position du véhicule est bien déterminée dans ce repère.Radar and lidar type sensors, typically used in remote detection applications such as ACC (for "Autonomous Cruise Control" in English) for example, directly provide information on the position and sometimes 3D speed of objects present in the scene. . This information makes it possible to segment the various objects present and to attribute useful information to them such as their position, possibly their speed, with their real coordinates in three dimensions, in relation to the vehicle equipped with the sensor in question, and in relation to a fixed reference, if the position of the vehicle is clearly determined in this frame.
Mais de tels capteurs n'offrent pas de champ de large, et leur positionnement angulaire n'est jamais très précis. Par ailleurs, de tels capteurs n'apportent aucune information sur l'environnement routier, telle que la position de véhicule sur sa voie, le nombre de voies, la trajectoire de la route, la classification des cibles, la possibilité de reconnaître des éléments de l'infrastructure tels que les panneaux... Par ailleurs, de tels capteurs longue portée à champ étroit ne peuvent discerner suffisamment tôt les scénarios dits de « cut-in », où un véhicule provenant d'une autre voie de circulation que celle du véhicule porteur vient s'intercaler devant celui-ci, et le conducteur doit savoir que la tâche de gérer un tel scénario lui incombe.But such sensors do not offer a wide field, and their angular positioning is never very precise. Furthermore, such sensors do not provide any information on the road environment, such as the position of the vehicle in its lane, the number of lanes, the trajectory of the road, the classification of targets, the possibility of recognizing elements of infrastructure such as panels... Moreover, such narrow-field long-range sensors cannot discern sufficiently early the so-called "cut-in" scenarios, where a vehicle coming from a traffic lane other than that of the carrier vehicle is inserted in front of it, and the driver must know that the task of managing such a scenario falls to him.
D’autres solutions mettent en œuvre des caméras embarquées à bord du véhicule.Other solutions implement cameras on board the vehicle.
Le document FR 2 938 227 A1 propose ainsi d'exploiter plusieurs images successives capturées par une telle caméra et d'établir une cartographie du flot optique dans la scène visualisée pour segmenter les obstacles offrant un certain comportement par rapport au véhicule porteur. Les véhicules dépassant ou croisant la trajectoire du véhicule porteur sont ainsi détectés.Document FR 2 938 227 A1 thus proposes to use several successive images captured by such a camera and to establish a map of the optical flow in the scene viewed in order to segment the obstacles offering a certain behavior with respect to the carrier vehicle. Vehicles overtaking or crossing the trajectory of the carrier vehicle are thus detected.
Ces dispositifs anticollision permettent d’alerter le conducteur sur le risque de collision en lui proposant une réponse d’évitement à effectuer. Certains d’entre eux peuvent même commander l’exécution par le véhicule hôte d’une réponse d’évitement lorsqu’un risque de collision avec un tel objet mobile est identifié.These anti-collision devices alert the driver to the risk of collision by suggesting an avoidance response to be made. Some of them can even command the execution by the host vehicle of an avoidance response when a risk of collision with such a moving object is identified.
Toutefois, les différents dispositifs anticollision présents dans les véhicules se trouvant dans zone considérées ne sont pas coordonnés entre eux, de sorte que les réponses d’évitement qu’ils déterminent chacun indépendamment l’un de l’autre peuvent avoir un effet néfaste et même provoquer des accidents.However, the various anti-collision devices present in the vehicles located in the zone considered are not coordinated with each other, so that the avoidance responses that they each determine independently of one another can have a harmful effect and even cause accidents.
La présente invention vise donc à améliorer la situation.The present invention therefore aims to improve the situation.
Elle propose à cet effet un procédé d’analyse prédictive en temps réel par un dispositif débarqué de la survenue d’un accident sur une zone de circulation routière accidentogène, caractérisé en ce qu’il comprend les étapes successives suivantes :
- émission, par ledit dispositif et via un protocole de communication sans fil, de requêtes de transmission des données de conduite à l’ensemble des véhicules présents dans ladite zone accidentogène ;
- réception, par ledit dispositif et via ledit protocole de communication sans fil, desdites données de conduite en provenance desdits véhicules présents dans ladite zone accidentogène ;
- détermination, par ledit dispositif et à partir des données de conduite reçues, du contexte de conduite courant dans ladite zone accidentogène ;
- estimation, par ledit dispositif et via un algorithme utilisant un réseau neuronal préalablement entrainé à partir d’une base d’apprentissage, du caractère à risque élevé d’accident ou non dudit contexte de conduite courant dans ladite zone accidentogène ; et
- transmission, par ledit dispositif et à chaque véhicule présent dans ladite zone accidentogène, d’un ordre d’émission d’une alerte à l’intention du conducteur de ce véhicule lorsque ledit contexte de conduite courant a été identifié comme à risque élevé d’accident.To this end, it proposes a method of predictive analysis in real time by a dismounted device of the occurrence of an accident in an accident-prone road traffic zone, characterized in that it comprises the following successive steps:
- emission, by said device and via a wireless communication protocol, of requests for transmission of driving data to all the vehicles present in said accident-prone zone;
- reception, by said device and via said wireless communication protocol, of said driving data from said vehicles present in said accident-prone zone;
- determination, by said device and from the driving data received, of the current driving context in said accident-prone zone;
- Estimation, by said device and via an algorithm using a previously trained neural network from a learning base, of the high accident risk character or not of said current driving context in said accident-prone zone; And
- transmission, by said device and to each vehicle present in said accident-prone zone, of an order to issue an alert to the driver of this vehicle when said current driving context has been identified as high risk of 'accident.
En analysant les données transmises par l’ensemble des véhicules présents sur la zone accidentogène, le procédé d’analyse prédictive selon l’invention permet de prédire avec une excellente fiabilité le risque de survenance d’un accident.By analyzing the data transmitted by all the vehicles present in the accident-prone area, the predictive analysis method according to the invention makes it possible to predict with excellent reliability the risk of an accident occurring.
Par ailleurs, en coordonnant les alertes émises à l’intention des conducteurs de ces véhicules lorsqu’un risque élevé d’accident est identifié, ce procédé permet en outre de limiter la réalisation par certains conducteurs d’actions délétères en réponse ces alertes.Furthermore, by coordinating the alerts issued to the drivers of these vehicles when a high risk of accident is identified, this process also makes it possible to limit the performance by certain drivers of harmful actions in response to these alerts.
Selon des caractéristiques préférées dudit procédé d’analyse prédictive selon l’invention :
- ledit algorithme utilise un réseau neuronal de régression généralisée ;
- ladite base d’apprentissage est constituée par une pluralité de couples de données comprenant chacun en entrée, les données représentatives d’un contexte de conduite passé dans ladite zone accidentogène, et en sortie, l’indication sur la survenue ou non d’un accident de la circulation dans un certain délai prédéterminé suivant ce contexte de conduite passé ;
- ledit réseau neuronal est configuré pour subir périodiquement un nouvel apprentissage à partir d’une base d’apprentissage mise en jour en y intégrant de nouveaux couples de données issus des données récoltées par ledit dispositif au cours du temps ;
- ledit algorithme est également apte à déterminer, lorsqu’il estime que le contexte de conduite courant est à risque élevé d’accident, l’action la plus adéquate à appliquer par le conducteur de chaque véhicule présent dans la zone accidentogène afin d’éviter la survenue d’un accident ;
- ladite alerte comprend, pour chacun des véhicules présents dans ladite zone accidentogène, l’affichage d’un message textuel approprié sur au moins un écran de ce véhicule ;
- ledit message textuel approprié indique ladite action la plus adéquate à appliquer par le conducteur ;
- ladite alerte comprend, pour chacun des véhicules présents dans ladite zone accidentogène, l’émission d’un signal sonore via la sirène de ce véhicule ; et/ou
- ledit protocole de communication sans fil est de type véhicule à tout (V2X).According to preferred characteristics of said predictive analysis method according to the invention:
- Said algorithm uses a generalized regression neural network;
- said learning base is made up of a plurality of pairs of data each comprising as input data representative of a past driving context in said accident-prone zone, and as output the indication of the occurrence or not of a traffic accident within a certain predetermined time following this past driving context;
- said neural network is configured to periodically undergo a new learning from an updated learning base by integrating therein new pairs of data from the data collected by said device over time;
- said algorithm is also capable of determining, when it considers that the current driving context is at high risk of accident, the most appropriate action to be applied by the driver of each vehicle present in the accident-prone zone in order to avoid the occurrence of an accident;
- said alert comprises, for each of the vehicles present in said accident-prone zone, the display of an appropriate text message on at least one screen of this vehicle;
- said appropriate text message indicates said most appropriate action to be applied by the driver;
- said alert comprises, for each of the vehicles present in said accident-prone zone, the emission of an audible signal via the siren of this vehicle; and or
- Said wireless communication protocol is of the vehicle-to-everything (V2X) type.
L’invention vise également sous un second aspect un dispositif d’alerte comportant une unité de pilotage dotée d’un calculateur associé à un module mémoire et apte à mettre en œuvre ledit procédé d’analyse prédictive.The invention also relates, in a second aspect, to an alert device comprising a control unit provided with a computer associated with a memory module and able to implement said predictive analysis method.
L’exposé de l’invention sera maintenant poursuivi par la description détaillée de plusieurs exemples de réalisation, donnée ci-après à titre illustratif mais non limitatif, en référence aux dessins annexés, sur lesquels :The description of the invention will now be continued with the detailed description of several embodiments, given below by way of illustration but not limitation, with reference to the appended drawings, in which:
Claims (10)
- émission, par ledit dispositif (10) et via un protocole de communication sans fil, de requêtes de transmission des données de conduite à l’ensemble des véhicules présents (5, 6, 7, 8) dans ladite zone accidentogène (1) (100) ;
- réception, par ledit dispositif (10) et via ledit protocole de communication sans fil, desdites données de conduite en provenance desdits véhicules présents (5, 6, 7, 8) dans ladite zone accidentogène (1) (200) ;
- détermination, par ledit dispositif (10) et à partir des données de conduite reçues, du contexte de conduite courant dans ladite zone accidentogène (1) (300) ;
- estimation, par ledit dispositif (10) et via un algorithme utilisant un réseau neuronal préalablement entrainé à partir d’une base d’apprentissage, du caractère à risque élevé d’accident ou non dudit contexte de conduite courant dans ladite zone accidentogène (1) (400) ; et
- transmission, par ledit dispositif (10) et à chaque véhicule (5, 6, 7, 8) présent dans ladite zone accidentogène, d’un ordre d’émission d’une alerte à l’intention du conducteur de ce véhicule lorsque ledit contexte de conduite courant a été identifié comme à risque élevé d’accident (500).Method of predictive analysis in real time by a dismounted device (10) of the occurrence of an accident in an accident-prone road traffic zone (1), characterized in that it comprises the following successive steps:
- emission, by said device (10) and via a wireless communication protocol, of requests for transmission of driving data to all the vehicles present (5, 6, 7, 8) in said accident-prone zone (1) ( 100);
- reception, by said device (10) and via said wireless communication protocol, of said driving data from said vehicles present (5, 6, 7, 8) in said accident-prone zone (1) (200);
- determination, by said device (10) and from the driving data received, of the current driving context in said accident-prone zone (1) (300);
- estimation, by said device (10) and via an algorithm using a previously trained neural network from a learning base, of the high accident risk character or not of said current driving context in said accident-prone zone (1 ) (400); And
- transmission, by said device (10) and to each vehicle (5, 6, 7, 8) present in said accident-prone zone, of an order to issue an alert for the driver of this vehicle when said current driving context was identified as high accident risk (500).
- en entrée, les données représentatives d’un contexte de conduite passé dans ladite zone accidentogène (1) ; et
- en sortie, l’indication sur la survenue ou non d’un accident de la circulation dans un certain délai prédéterminé suivant ce contexte de conduite passé.Method of predictive analysis according to one of Claims 1 or 2, characterized in that the said learning base consists of a plurality of pairs of data each comprising:
- as input, the data representative of a past driving context in said accident-prone zone (1); And
- at the output, the indication as to whether or not a traffic accident has occurred within a certain predetermined period according to this past driving context.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FR2113254A FR3130433A1 (en) | 2021-12-10 | 2021-12-10 | Process for the real-time predictive analysis of the occurrence of an accident in an accident-prone area |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FR2113254 | 2021-12-10 | ||
| FR2113254A FR3130433A1 (en) | 2021-12-10 | 2021-12-10 | Process for the real-time predictive analysis of the occurrence of an accident in an accident-prone area |
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| Publication Number | Publication Date |
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| FR3130433A1 true FR3130433A1 (en) | 2023-06-16 |
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| Application Number | Title | Priority Date | Filing Date |
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| FR2113254A Pending FR3130433A1 (en) | 2021-12-10 | 2021-12-10 | Process for the real-time predictive analysis of the occurrence of an accident in an accident-prone area |
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| FR (1) | FR3130433A1 (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2938227A1 (en) | 2008-11-10 | 2010-05-14 | Peugeot Citroen Automobiles Sa | Obstacle detecting method for motor vehicle, involves temporally identifying closed contour blobs among averaged interest zones, and comparing identified blobs to obstacles for considered motor vehicle |
| US20190164422A1 (en) * | 2017-11-28 | 2019-05-30 | Honda Motor Co., Ltd. | System and method for providing an infrastructure based safety alert associated with at least one roadway |
| US20210020042A1 (en) * | 2019-04-26 | 2021-01-21 | Blackberry Limited | Method and system for traffic behavior detection and warnings |
| US20210241624A1 (en) * | 2020-01-31 | 2021-08-05 | Toyota Motor Engineering & Manufacturing North America, Inc. | Edge-assisted alert system |
| FR3110998A1 (en) * | 2020-05-26 | 2021-12-03 | Psa Automobiles Sa | A method of regulating road traffic on the outskirts of a traffic lane crossing area. |
-
2021
- 2021-12-10 FR FR2113254A patent/FR3130433A1/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2938227A1 (en) | 2008-11-10 | 2010-05-14 | Peugeot Citroen Automobiles Sa | Obstacle detecting method for motor vehicle, involves temporally identifying closed contour blobs among averaged interest zones, and comparing identified blobs to obstacles for considered motor vehicle |
| US20190164422A1 (en) * | 2017-11-28 | 2019-05-30 | Honda Motor Co., Ltd. | System and method for providing an infrastructure based safety alert associated with at least one roadway |
| US20210020042A1 (en) * | 2019-04-26 | 2021-01-21 | Blackberry Limited | Method and system for traffic behavior detection and warnings |
| US20210241624A1 (en) * | 2020-01-31 | 2021-08-05 | Toyota Motor Engineering & Manufacturing North America, Inc. | Edge-assisted alert system |
| FR3110998A1 (en) * | 2020-05-26 | 2021-12-03 | Psa Automobiles Sa | A method of regulating road traffic on the outskirts of a traffic lane crossing area. |
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