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EP-4082862-B1 - PLATFORM FOR PATH PLANNING SYSTEM DEVELOPMENT FOR AUTOMATED DRIVING SYSTEM

EP4082862B1EP 4082862 B1EP4082862 B1EP 4082862B1EP-4082862-B1

Inventors

  • GYLLENHAMMAR, MAGNUS
  • ZANDÉN, Carl
  • KHORSAND VAKILZADEH, Majid

Dates

Publication Date
20260513
Application Date
20210429

Claims (6)

  1. A method (100) performed by an in-vehicle computing system for automated development of a path planning module of a vehicle, wherein the vehicle is equipped with an Automated Driving System, ADS, the method comprising: obtaining (101) a candidate path from the path planning module, wherein the path planning module is configured to generate the candidate path for the vehicle based on a path planning model and data indicative of the surrounding environment of the vehicle; obtaining (102) a reference framework for evaluating the candidate path, the reference framework being configured to indicate one or more risk values associated with the candidate path when the candidate path is applied in the reference framework; wherein obtaining (102) the reference framework comprises: storing (111), during a time period, a set of perception data obtained from a perception system of the vehicle, the perception system being configured to generate the set of perception data based on sensor data obtained from one or more vehicle-mounted sensors during the time period, forming (112), by post-processing the set of perception data, a baseline worldview indicative of a scenario in the surrounding environment of the vehicle during the time period, and wherein the baseline worldview forms the reference framework; evaluating (103) the obtained candidate path by applying the candidate path in the reference framework in order to determine a cost function based on the one or more risk values, the cost function being indicative of a performance of the path planning module within the reference framework; wherein evaluating (103) the obtained candidate path comprises: comparing (113) the candidate path with the baseline worldview in order to obtain the one or more risk values, and determining (114) the cost function based on the obtained one or more risk values, each risk value being indicative of a temporal evolution of a collision threat measure for the candidate path during at least a portion of the time period; updating (104) one or more parameters of the path planning model by means of an optimization algorithm configured to optimize the determined cost function.
  2. The method (100) according to claim 1, further comprising: transmitting (105) the one or more updated parameters of the path planning model of the path planning module to a remote entity; receiving (106) a set of globally updated parameters of the path planning model of the path planning module from the remote entity, wherein the set of globally updated parameters are based on information obtained from a plurality of vehicles comprising a corresponding path planning module; updating (107) the path planning model of the path planning module based on the received set of globally updated parameters.
  3. A computer-readable storage medium storing one or more programs configured to be executed by one or more processors of an in-vehicle processing system, the one or more programs comprising instructions for performing the method according to any one of the preceding claims.
  4. An apparatus (10) for automated development of a path planning module (73) of a vehicle (1), wherein the vehicle is equipped with an Automated Driving System, ADS, the apparatus comprising control circuitry (11) configured to: obtain a candidate path from the path planning module (73), wherein the path planning module is configured to generate the candidate path for the vehicle based on a path planning model and data indicative of the surrounding environment of the vehicle; obtain a reference framework for evaluating the candidate path, the reference framework being configured to indicate one or more risk values associated with the candidate path when the candidate path is applied in the reference framework; wherein obtaining (102) the reference framework comprises: storing (111), during a time period, a set of perception data obtained from a perception system of the vehicle, the perception system being configured to generate the set of perception data based on sensor data obtained from one or more vehicle-mounted sensors during the time period, forming (112), by post-processing the set of perception data, a baseline worldview indicative of a scenario in the surrounding environment of the vehicle during the time period, and wherein the baseline worldview forms the reference framework; evaluate the obtained candidate path by applying the candidate path in the reference framework in order to determine a cost function based on the one or more risk values, the cost function being indicative of a performance of the path planning module (73) within the reference framework; wherein evaluating (103) the obtained candidate path comprises: comparing (113) the candidate path with the baseline worldview in order to obtain the one or more risk values, and determining (114) the cost function based on the obtained one or more risk values, each risk value being indicative of a temporal evolution of a collision threat measure for the candidate path during at least a portion of the time period; update one or more parameters of the path planning model by means of an optimization algorithm configured to optimize the determined cost function.
  5. The apparatus (10) according to claim 4, wherein the control circuitry (11) is further configured to: transmit the one or more updated parameters of the path planning model of the path planning module to a remote entity (2); receive a set of globally updated parameters of the path planning model of the path planning module from the remote entity (2), wherein the set of globally updated parameters are based on information obtained from a plurality of vehicles comprising a corresponding path planning module; update the path planning model of the path planning module based on the received set of globally updated parameters.
  6. A vehicle (1) comprising: a set of vehicle-mounted sensors (6a, 6b, 6c) configured to monitor a surrounding environment of the vehicle; an automated driving system, ADS, having a perception system (6, 78) configured to generate perception data based on sensor data obtained from one or more of the set of vehicle-mounted sensors; a path planning module (73) configured to generate the candidate path for the vehicle; an apparatus (10) according to any one of claims 4 - 5.

Description

TECHNICAL FIELD The present invention relates to a method for performance evaluation and development of a path planning module of a vehicle equipped with an Automated Driving System (ADS). In particular, the present invention relates to open-loop evaluation of a path planning module of a vehicle, and subsequent updating thereof. BACKGROUND During the last few years, the research and development activities related to autonomous vehicles has exploded in number and many different approaches are being explored. An increasing portion of modern vehicles have advanced driver-assistance systems (ADAS) to increase vehicle safety and more generally road safety. ADAS - which for instance may be represented by adaptive cruise control, ACC, collision avoidance system, forward collision warning, etc. - are electronic systems that may aid a vehicle driver while driving. Today, there is ongoing research and development within a number of technical areas associated to both the ADAS and Autonomous Driving (AD) field. ADAS and AD will herein be referred to under the common term Automated Driving System (ADS) corresponding to all of the different levels of automation as for example defined by the SAE J3016 levels (0 - 5) of driving automation, and in particular for level 4 and 5. In a not too distant future, ADS solutions are expected to have found their way into a majority of the new cars being put on the market. An ADS may be construed as a complex combination of various components that can be defined as systems where perception, decision making, and operation of the vehicle are performed by electronics and machinery instead of a human driver, and as introduction of automation into road traffic. This includes handling of the vehicle, destination, as well as awareness of surroundings. While the automated system has control over the vehicle, it allows the human operator to leave all or at least some responsibilities to the system. An ADS commonly combines a variety of sensors to perceive the vehicle's surroundings, such as e.g. radar, LIDAR, sonar, camera, navigation system e.g. GPS, odometer and/or inertial measurement units (IMUs), upon which advanced control systems may interpret sensory information to identify appropriate navigation paths, as well as obstacles, free-space areas, and/or relevant signage. Much of the current efforts for development of ADSs revolves around safely launching a first system to the market. However, once that is achieved it will be paramount to improve the system in a safe and efficient manner, both to achieve cost reductions as well as performance improvements. Generally, there are significant costs associated with the development, testing, and validation of safety of the ADS (or of "ADS features"), especially related to field tests and the understanding of how the system behaves in traffic. Moreover, there are additional challenges in terms of managing the immense amounts of data generated by ADS equipped vehicles in order to develop, test and verify various ADS features, not only from a data storage, processing and bandwidth perspective, but also from a data security/privacy perspective. There is accordingly a need in the art for new solutions for facilitating development, testing, and/or validation of ADSs in order to continuously be able to provide safer and more performant systems. As always, the improvements shall preferably be made without significant impact on the size, power consumption and cost of the on-board system or platform. US 2020/0307561 A1 discloses systems and methods for pre-decision manoeuvre risk planning, and in particular for generating fuller representations of uncertainty indicators for use in pre-decision manoeuvre risk planning under conditions of reduced sensed information. In more detail, the system of US 2020/0307561 A1 includes one or more modules configured to apply functions to sensed data of the environment for configuring a machine learning (ML) model of decision making behaviour of the vehicle by an action risk assessment model trained use semi-supervised ML techniques by online and offline training for mapping function to candidate actions to determine with risk factors a learned drivable path. US 2019/0302767 A1 discloses a temporal prediction model for semantic intent understanding. In more detail, an agent (e.g., a moving object) in an environment can be detected in sensor data collected from sensors on a vehicle. Computing device(s) associated with the vehicle can determine, based partly on the sensor data, attribute(s) of the agent (e.g. classification, position, velocity, etc.), and can generate, based partly on the attribute(s) a temporal prediction model, semantic intent(s) of the agent (e.g., crossing a road, staying straight, etc.), which can correspond to candidate trajectories of the agent. These candidate trajectories can be associated with weight(s) representing likelihood(s) that the agent will perform respective intent(s). The computing devic