Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios

Stahl, Tim; Wischnewski, Alexander; Betz, Johannes; Lienkamp, Markus

doi:10.1109/itsc.2019.8917032

Cited by 52 publications

(45 citation statements)

References 14 publications

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“…The architecture of the OV follows previous findings [44]. Accordingly, the OV runs in parallel with the planning module (Fig.…”

Section: B Implementation (S-2)mentioning

confidence: 94%

“…To this end, a structured approach should be pursued to identify a holistic list of criteria for a safe trajectory. In the aforementioned paper [44], we introduced one possible approach based on interfaces between SW modules. Resulting key criteria derived in the referenced work are listed in Tab.…”

Section: A Requirements (S-1)mentioning

confidence: 99%

“…In addition to scenario-based tests with deliberate fault injection, long runs in a real environment are essential to verify low false alarms. In this course, we used the data of a trajectory planner [44] in autonomous races to evaluate the Supervisor. As an example, we elaborate a race with 10 laps and two cars on a racetrack in Modena, Italy (Fig.…”

Section: B Field-test Evaluation -Real-world Examplementioning

confidence: 99%

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Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

Stahl

Diermeyer

2021

IEEE Open J. Intell. Transp. Syst.

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Safety guarantees and regulatory approval for autonomous vehicles remain an ongoing challenge. In particular, software that is frequently adapted or contains complex, non-transparent components, such as artificial intelligence, is exceeding the limits of safety standards. This paper presents a detailed implementation of an online verification module -the Supervisor -that copes with these challenges. The presented implementation focuses on autonomous race vehicles without loss of generality. Following an identified holistic list of safety-relevant requirements for a trajectory, metrics are developed to monitor whether the trajectory can safely be executed. To evaluate safety with respect to dynamic objects in a semi-structured and highly dynamic racing environment, rule-based reachable sets are presented. As a result, the pure reachable set is further constrained by applicable regulations. Real-time capability and effectiveness are demonstrated in fault-injected scenario-based tests and on real-world run data. The implemented Supervisor will be publicly available on GitHub.

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“…The architecture of the OV follows previous findings [44]. Accordingly, the OV runs in parallel with the planning module (Fig.…”

Section: B Implementation (S-2)mentioning

confidence: 94%

Section: A Requirements (S-1)mentioning

confidence: 99%

Section: B Field-test Evaluation -Real-world Examplementioning

confidence: 99%

See 1 more Smart Citation

Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

Stahl

Diermeyer

2021

IEEE Open J. Intell. Transp. Syst.

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“…When driving autonomously, the local trajectory planner used the global raceline as a reference for the fastest solution. However, if there were obstacles on the track or if the car wanted to perform an overtaking maneuver, it dynamically generated several local paths on the basis of pre-sampled splines [32]. The required information about objects on the track were gathered by a dynamic object list in submodule two.…”

Section: The Autonomous Software Stack From Tummentioning

confidence: 99%

Autonomous Driving—A Crash Explained in Detail

et al. 2019

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Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstancescausing the crash are elaborated in detail. By presented and explaining in detail which softwarepart failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous.

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“…Hu et al [32] presented a real-time dynamic path planning method; i.e., an optimal path with appropriate acceleration and velocity profiles is the result. A graph-based planning method to generate an actionable set of multiple drivable trajectories for race vehicles, is found in [33]. For road vehicles, Werling et al [34] proposed an optimal trajectory generation method, with which velocity maintenance, merging, following, stopping and a reactive collision avoidance functionality were achieved.…”

Section: Introductionmentioning

confidence: 99%

The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

Németh

Gáspár

2021

Applied Sciences

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The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.

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Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios

Cited by 52 publications

References 14 publications

Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

Autonomous Driving—A Crash Explained in Detail

The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

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