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EP3882881B1 - Service de vitesse recommandée - Google Patents

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Publication number
EP3882881B1
EP3882881B1 EP20163939.0A EP20163939A EP3882881B1 EP 3882881 B1 EP3882881 B1 EP 3882881B1 EP 20163939 A EP20163939 A EP 20163939A EP 3882881 B1 EP3882881 B1 EP 3882881B1
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EP
European Patent Office
Prior art keywords
recommended speed
recommended
speed
speed data
vehicle
Prior art date
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Application number
EP20163939.0A
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German (de)
English (en)
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EP3882881A1 (fr
Inventor
Peter Lindskog
Erik Wernholt
Per Cronvall
Gustav Jagbrant
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Magna Electronics Sweden AB
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Magna Electronics Sweden AB
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Priority to EP20163939.0A priority Critical patent/EP3882881B1/fr
Publication of EP3882881A1 publication Critical patent/EP3882881A1/fr
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Publication of EP3882881B1 publication Critical patent/EP3882881B1/fr
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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]
    • 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
    • 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
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • 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

Definitions

  • End-to-End-Learning systems for autonomous driving that have been suggested in recent years. These systems try to solve the more difficult general sensing and steering problems. However, none of these systems have been demonstrated to work reliably enough to replace more modular and hand-engineered systems. As such they seem unlikely to be employed in a near future.
  • Map service providers use a map as a foundation for their services.
  • One way for the service provider to provide the map service is using a cloud, but other possibilities than using a cloud may exist.
  • data is often organized in layers containing different type of information that can be combined in different ways.
  • the road network divided into suitable road segments is a basic such layer.
  • Other layers may contain information about speed limits, traffic signs, landmarks, road condition (smooth, rough, etc.), road cover (wet, snow, ice, etc.) slipperiness, and so on.
  • the "appropriate" speed to drive may change suddenly. This can take some time to detect if statistics are based on actual speed from vehicles, especially if the driving pattern varies substantially .
  • US 2020/0050209 A1 discloses a system and method for deriving speed limits of road segments by mining large-scale vehicle data traces. The current location of vehicles and a corresponding road segment are determined, the speed data is received and accumulated to a speed distribution function for the road segment and a speed limit range is estimated.
  • the problem underlying the present invention is to develop and provide a driver assistance system and service adapted to regulate the vehicle speed to be appropriate for the current driving conditions in an efficient and reliable manner.
  • a method for providing recommended speed data to vehicles via a map service provider is provided according to claim 1.
  • the inventive approach to solve the problem is to use recommended speed data instead of actual speed of the transmitting vehicles.
  • the algorithm for holistic estimation of an recommended vehicle speed produces a recommended speed estimation for the ego vehicle.
  • This may be achieved for example by a machine learning model adapted to estimate recommended speed for the ego vehicle by training images recorded by an imaging apparatus of a test vehicle.
  • the algorithm may be capable to sort out a variance of inappropriate speed data caused by unforeseen influences. For example this may include the reduction of speed to exit the road, reducing the speed in order to keep a distance to a preceding vehicle, parking the vehicle, making a u-turn, disregard of speed limits, etc. Therefore, data selection and preparation is already performed by the algorithm of the ego vehicle and culminates in the recommended speed data provided by the algorithm.
  • a complete traditional Adaptive Cruise Control-system (ACC), potentially based on one or multiple sensors such as camera(s) and/or radar(s), may be run on the entire available dataset. The situations where the ACC system does not perform an explicit action are then used as training data.
  • a conventional Adaptive Cruise Control system may preferably be run on the training data set, and images are used as training data where the conventional Adaptive Cruise Control system does not perform an explicit action.
  • the recommended speed data transmitted may, in its simplest form, provide the recommended speed as is, e.g. a number, possibly an integer, and a position information to combine the speed to a corresponding road segment.
  • Aggregated recommended speed data may be provided by computing the mean recommended speed value from the N last received messages.
  • Map service providers usually have an infrastructure for efficiently passing (either directly or via transponders) messages of this kind to vehicles.
  • time filtering is applied to a plurality of recommended speed messages to obtain time-filtered aggregated recommended speed data.
  • messages are specific for a certain time period, e.g. to handle rush hours. Therefore, applying time filters allows to build histograms over different time periods, for example each hour and each week day separately, and use that to capture dynamic differences.
  • the speed estimation algorithm outputs not only a speed but also a confidence measure of the estimate.
  • This confidence measure can then be used by the deployed ACC system when combining it with other speed information.
  • the speed estimation algorithm preferably outputs a confidence measure of the recommended speed estimate.
  • Sending a confidence level allows the cloud service to weight information sent to it.
  • the confidence level can be binary, e.g. recommended speed is uncertain or certain.
  • the cloud service may choose to ignore messages, e.g. in case the road ahead is unexpectedly blocked for some reason (e.g. due to a queue).
  • the value sent can be a confidence level specifying to what degree the data can be trusted.
  • the recommended speed message contains an estimate of the validity duration of the recommended speed. For example, if the vehicle is blocked by another vehicle, the duration could be in minutes, if there is heavy rain it could be hours, or if a road work area is entered the duration can be several days or even weeks.
  • the recommended speed service contains information from specific scene classifiers computed by vehicles. Any means that provide scene understanding can be used, e.g. vehicles equipped with wetness sensors or equipped with an imaging apparatus. Vehicles with a camera or other imaging sensors typically compute several features. Scene classifiers are used to determine different environmental factors: brightness (e.g. dark,..., light), road surface (e.g. asphalt, concrete, gravel), road cover (e.g. dry, wet, snow, ice), driving condition (raining, snowing, fog, etc.) and so on. Preferably this type of data is included in the transmitted speed messages to the cloud/map service provider. The service can then compute and transmit condition specific aggregated statistics to the vehicles, which allows refined and situation adapted aggregated recommended speed suggestions.
  • scene classifiers are used to determine different environmental factors: brightness (e.g. dark,..., light), road surface (e.g. asphalt, concrete, gravel), road cover (e.g. dry, wet, snow, ice), driving condition (raining, snowing, fog, etc.)
  • a recommended speed distribution is used and sent to the service provider instead of a single recommended speed value to capture multiple hypotheses (e.g. grouped into categories such as continue straight on or turn left with different recommended speed values, or directly referring to different road segments of the map).
  • the cloud can build aggregated statistics on this as well and make more advanced aggregated recommended speed suggestions.
  • Preferably information from several map layers is used to compute aggregated recommended speed messages.
  • One option to use combined information from several map layers in the cloud is to use slipperiness information if that is available for the road segments. The aggregated recommended speed could then be further reduced if it is determined that slipperiness is not already considered in the recommended speed computation.
  • Vehicles equipped with a holistic speed adaption algorithm preferably combine in-vehicle recommended speed data and received aggregated recommended speed data. Vehicles without this algorithm must rely on the aggregated recommended speed value sent to it. If this vehicle can determine brightness, road surface, road cover, and so on, it can use this information to determine a refined condition specific recommended speed value as well. If this is not possible, other sensor information, e.g. ambient temperature can be used to guide in the choice of recommended speed.
  • sensor information e.g. ambient temperature can be used to guide in the choice of recommended speed.
  • Another preferred embodiment is to provide refined condition specific recommended speed from the ego vehicle to another vehicle via car-to-car communication.
  • a vehicle with the ability to compute the driving condition has passed a specific road segment recently, then this can be provided to another vehicle for usage either via transponder or via the cloud, to allow the other vehicle to use a refined condition specific speed recommendation.
  • This option is especially beneficial for providing recommended speed to vehicles lacking the cloud service.
  • the training data is filtered and grouped based on driver characteristics such as cautious, normal, dynamic.
  • driver characteristics such as cautious, normal, dynamic.
  • This system itself could output speed recommendations corresponding to different driver characteristic modes. These recommendations are transmitted to the service provider, which uses this to generate aggregated recommended speed for the different modes.
  • the service provider generates (in the cloud) the recommendation for the various modes. This could be done through any statistical model or set of rules, e.g., in the simplest case the aggregated recommended speed for the cautious mode could be 0.9 times that of the normal mode.
  • An in-vehicle system modifies the received aggregated recommended speed and modifies it according to any statistical model or set of rules, e.g. in the simplest case the aggregated recommended speed for the cautious mode could again be 0.9 times that of the normal mode.
  • a further preferred embodiment of the invention is to transmit both recommended speed and actual speed of a vehicle to the cloud, possibly with information of the kind outlined above (confidence level, scene classification information, etc.)
  • a straight forward approach is just to weight these together, e.g. let the aggregated recommended speed to be transmitted to vehicles for a road segment be formed as X * aggregated recommended speed + 1 ⁇ X * aggregated actual speed where X is a fixed value between 0 and 1.
  • X can also be adaptively chosen. For example if the variance of the aggregated actual speed is large, then trust the aggregated recommended speed more, i.e. let X be close to 1 in such case.
  • N1 is a weighting factor (>0; >1 if aggregated recommended speed is weighted higher than aggregated actual speed).
  • N1 and N2 can be restricted to some maximum values, and one can choose to only use the most recent messages available in the aggregation computations. Therefore a fast but reliable adaption to changes of a road segments is possible.
  • Yet another preferred embodiment is to combine aggregated recommended speed information with road information from the map as such, like sharp left bend, and so on. Fusing map information, aggregated recommended speed and aggregated actual speed is a further preferred possibility.
  • an automatic speed control system comprising an ACC function adapted to control the speed of the motor vehicle
  • said ACC function is adapted to use in-vehicle speed estimation and received aggregated speed data in said speed control.
  • the speed control system outputs a predicted speed to be used as a set speed or desired speed. The predicted speed is fed to an ACC system that determines a suitable action.
  • Figure 1 shows the basic framework of the invention.
  • Recommended Speed Messages 1 are transmitted to the service provider 2 and aggregated recommended speed messages 3 are transmitted to vehicles 4, 5 subscribing to the recommended speed data service.
  • the first class of vehicles 4 run a Holistic Speed Adaption algorithm and provide and receive aggregated recommended speed data 3 from the service provider, whereas the second class of vehicles 5 are other vehicles just subscribing to the aggregated recommended speed data 3.
  • the aggregated speed data 3 transmitted to the vehicles 4, 5 can contain single road segment 6 data or a set of such data for road segments 6 in a region around each vehicle 4, 5, thus enabling look-ahead based determination of the recommended speed, as well as more efficient route planning.
  • any other algorithm providing recommended speed 1 can be used in the suggested framework, since any in-vehicle computed recommended speed 1 has a lower variance and higher situation-adapted accuracy than the pure data of actual driver speed.
  • the preferred embodiment is a computational node in a vehicle 4 that runs a Holistic Speed Adaption algorithm and sends recommended speed information to a service provider 2, for example wirelessly via transponders 7 or directly via a communication link.
  • Information is sent on a regular basis, for example every X second or every Y meter driven.
  • the message contains information about the location of the vehicle 4 and the recommended speed computed by the Holistic Speed Adaption algorithm.
  • the recommended speed 1 transmitted can be the recommended speed as a number, possibly an integer, or simply a range information, e.g. "four" representing the recommended speed interval 40-50 km/h.
  • Recommended speed messages 1 are received by the service provider 2 from a fleet of vehicles 4 equipped with the Holistic Speed Adaption algorithm.
  • the location of the vehicle encoded in a message is mapped to a road segment 6 by a standard location-to-road-segment mapping algorithm, and the recommended speed information received is saved in a database 8.
  • a mapping between road segment 6 and recommended speed is saved.
  • the available recommended speed data are used to compute an aggregated recommended speed, for example, by computing the mean recommended speed value from the N last received messages.
  • the cloud 9 it is also possible to understand that messages are specific for a certain time period, e.g. to handle rush hours. Therefore, it makes sense to build histograms over different time periods and to use that to capture dynamic differences.
  • the invention is applicable to autonomous driving, where the ego vehicle is an autonomous vehicle adapted to drive partly or fully autonomously or automatically, and driving actions of the driver are partially and/or completely replaced or executed by the ego vehicle.
  • an automatic speed control system for a motor vehicle or a semi-automatic driver assistance system comprises an Automatic Cruise Control (ACC) function, which is running on an in-vehicle data processing device. This device may process data from several in-vehicle sensors, e.g. imaging sensors, cameras, lidars, etc.
  • the ACC function is adapted to control the speed of the motor vehicle depending on the in-vehicle speed estimation of an appropriate recommended speed and received aggregated recommended speed data, as described in the foregoing.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Traffic Control Systems (AREA)

Claims (14)

  1. Procédé pour fournir des données de vitesse recommandée à des véhicules par l'intermédiaire d'un fournisseur de services cartographiques (2),
    dans lequel chaque véhicule ego d'une flotte de véhicules (4) est adapté à exécuter un algorithme d'estimation holistique d'une vitesse de véhicule recommandée, dans lequel l'algorithme d'estimation holistique d'une vitesse de véhicule recommandée produit une estimation de vitesse recommandée pour le véhicule ego, et à transmettre des messages de vitesse recommandée (1) au fournisseur de services cartographiques (2), lesdits messages de vitesse recommandée comprenant lesdites données de vitesse recommandée estimées et la localisation du véhicule ego,
    dans lequel lesdits messages de vitesse recommandée reçus par le fournisseur de services cartographiques (2) sont mis en correspondance avec un segment de route correspondant (6) et enregistrés dans une base de données (8),
    dans lequel le fournisseur de services cartographiques (2) est adapté à calculer une donnée de vitesse recommandée agrégée pour chaque segment de route correspondant (6) sur la base des données de vitesse recommandée enregistrées et à transmettre lesdites données de vitesse recommandée agrégées sous la forme d'un message de vitesse recommandée agrégé (3) aux véhicules abonnés à un service de cartographie.
  2. Procédé selon la revendication 1,
    dans lequel un filtrage temporel est appliqué à une pluralité de messages de vitesse recommandée (1) pour obtenir des données de vitesse recommandée agrégées filtrées dans le temps (3).
  3. Procédé selon la revendication 1 ou 2,
    dans lequel le message de vitesse recommandée (1) contient un niveau de confiance de l'estimation de vitesse recommandée.
  4. Procédé selon l'une des revendications précédentes,
    dans lequel le message de vitesse recommandée (1) contient une estimation de la durée de validité de la vitesse recommandée.
  5. Procédé selon l'une des revendications précédentes,
    dans lequel le message de vitesse recommandée (1) contient des informations provenant de classificateurs de scène spécifiques calculés par des véhicules équipés d'une compréhension de scène basée sur des capteurs.
  6. Procédé selon l'une des revendications précédentes,
    dans lequel les messages de vitesse recommandée agrégés (3) contiennent une distribution de vitesse recommandée agrégée.
  7. Procédé selon l'une des revendications précédentes,
    dans lequel des informations provenant de plusieurs couches cartographiques sont utilisées pour calculer des données de vitesse recommandée agrégées (3).
  8. Procédé selon l'une des revendications précédentes,
    dans lequel les véhicules ego (4) équipés dudit algorithme d'adaptation de vitesse holistique combinent des données de vitesse recommandées à bord du véhicule et des données de vitesse recommandées agrégées reçues (3).
  9. Procédé selon l'une des revendications précédentes,
    dans lequel des informations relatives aux conditions de la route et/ou de capteurs à bord du véhicule sont combinées aux données de vitesse agrégées reçues (3) pour calculer une vitesse recommandée redéfinie spécifique aux conditions.
  10. Procédé selon la revendication 9,
    dans lequel lesdites données de vitesse recommandées spécifiques aux conditions sont transmises à partir du véhicule calculateur à un autre véhicule par l'intermédiaire d'une communication de voiture à voiture.
  11. Procédé selon l'une des revendications précédentes,
    dans lequel les données de vitesse recommandée sont filtrées et regroupées sur la base de caractéristiques du conducteur.
  12. Procédé selon la revendication 11,
    dans lequel des caractéristiques du conducteur sont regroupées dans des modes sélectionnables suivants : prudent, normal et dynamique.
  13. Procédé selon l'une des revendications précédentes,
    dans lequel des messages de vitesse recommandée transmis contiennent des données de vitesse recommandée et des données de vitesse réelle et des données de vitesse recommandée agrégées (3) sont calculées en pondérant les données de vitesse recommandée et les données de vitesse réelle.
  14. Procédé selon l'une des revendications précédentes,
    dans lequel le véhicule comprend un système de commande automatique de vitesse avec une fonction ACC adaptée à commander la vitesse du véhicule en fonction de l'estimation de vitesse à bord du véhicule et desdites données de vitesse recommandées agrégées reçues (3).
EP20163939.0A 2020-03-18 2020-03-18 Service de vitesse recommandée Active EP3882881B1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP20163939.0A EP3882881B1 (fr) 2020-03-18 2020-03-18 Service de vitesse recommandée

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP20163939.0A EP3882881B1 (fr) 2020-03-18 2020-03-18 Service de vitesse recommandée

Publications (2)

Publication Number Publication Date
EP3882881A1 EP3882881A1 (fr) 2021-09-22
EP3882881B1 true EP3882881B1 (fr) 2024-07-24

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Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8494759B2 (en) * 2010-09-08 2013-07-23 Toyota Motor Engineering & Manufacturing North America, Inc. Vehicle speed indication using vehicle-infrastructure wireless communication
US9547989B2 (en) * 2014-03-04 2017-01-17 Google Inc. Reporting road event data and sharing with other vehicles
US9970780B2 (en) * 2015-11-19 2018-05-15 GM Global Technology Operations LLC Method and apparatus for fuel consumption prediction and cost estimation via crowd sensing in vehicle navigation system
JP6717778B2 (ja) * 2017-05-15 2020-07-08 トヨタ自動車株式会社 道路リンク情報更新装置及び車両制御システム
US10838423B2 (en) * 2018-08-07 2020-11-17 GM Global Technology Operations LLC Intelligent vehicle navigation systems, methods, and control logic for deriving road segment speed limits

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