A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling

Betz, Johannes; Wischnewski, Alexander; Heilmeier, Alexander; Nobis, Felix; Hermansdorfer, Leonhard; Stahl, Tim; Herrmann, Thomas; Lienkamp, Markus

doi:10.1109/iccve45908.2019.8965238

Cited by 34 publications

(22 citation statements)

References 29 publications

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“…Tire performance assessment: the main goal of the tire performance assessment is to obtain information about the friction potential of the vehicle's tire-road contact, to store these data in backend maps and to provide it where required [10]. With this knowledge, the dynamic-trajectory-planning algorithms can calculate an adjusted path and velocity profile taking the vehicle's local maximum lateral and longitudinal acceleration into account [10]. By using vehicle-internal state variables (accelerations, forces, and torques) and an extended Kalman filter for estimating (partially) unobserved state variables, the current tire-road friction potential can be estimated.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the algorithm collects data over a certain period of time to calculate a mean value of the friction potential for fixed road segments. A detailed explanation of this proposed software can be found in [10,41].…”

Section: Discussionmentioning

confidence: 99%

“…The events could be classic race formats similar to Formula 1 (testing; qualifying; race), precision races similar to rally events, or hillclimb events. For these events, the Technical University of Munich (TUM) team developed an entire autonomous software stack that includes different software modules [9,10]: a perception part for recognizing static and dynamic objects, a dynamic trajectory and behavior planner for high-speed overtaking maneuvers, and a highly adapted control module. The basis for this software structure was inspired by Pendelton et al [11].…”

Section: Related Workmentioning

confidence: 99%

“…For this autonomous racecar our team from TUM developed a holistic autonomous driving software. A thorough description is given in the papers [9,10]. An overview of the software architecture with its seven sub-modules is displayed in Figure 2 in sufficient detail.…”

Section: The Autonomous Software Stack From Tummentioning

confidence: 99%

“…Obviously, this leads to a mismatch between planning and realization of the planned trajectory. To mitigate this uncertainty, it is required to include safety margins in terms of maximum accelerations as well as safety distance to the track boundaries [10]. However, it is unclear a priori how to set them as they depend on the controller performance and the environment conditions [10].…”

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Autonomous Software Stack From Tummentioning

confidence: 99%

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Autonomous Driving—A Crash Explained in Detail

et al. 2019

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TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

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For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 2700.25em km h − 1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 280.25em m s − 2 $28\,ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts.

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