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Autonomous cars represent the future of human transportation. We contribute to this course by cooperation with the automotive industry on making a Porsche Panamera car drive autonomously. Unfortunately, performing experiments with real cars is expensive, potentially dangerous, and sometimes not convenient. For this reason, we prototype many of the algorithms for the Panamera on an RC-car (a scaled-down model of autonomous car), which makes many experiments easier to conduct. To validate our work and to compare it with others, we participate in the International F1/10 Autonomous Racing Competition. In this racing event, student teams from all over the world compete in a so-called battle of algorithms. When developing these algorithms or combining a few of them in more complex control approaches, it is not always easy to decide which approach is better. In this paper, we present a comprehensive methodology for comparing the control approaches. We selected the algorithms for the comparison from the list of state-of-the-art approaches as well as approaches used by the teams within the competition. The compared algorithms include: - Iterative Follow The Gap algorithm, - Iterative Follow The Gap algorithm extended with location-based information (Map-based Follow The Gap), and - Static Optimal Trajectory Tracking algorithm. We compare the approaches above directly on the platform used in the competition rather than in simulations. Our evaluation is supported by a high-precision external localization system. We use the comparison criteria, selected based on our years-long experience, and which have proven themselves essential for successful participation in the competition. The criteria are: - racing performance (i.e., how fast is the car able to drive through the track), - obstacle avoidance slowdown (i.e., how much is the performance of the approach penalized by the presence of static obstacles), - hardware load (computing resources on the platform are limited; as the platform is selected by the organizers of the event), and - configuration effort (i.e., how much effort does it take to tune the parameters of the approach). The results of this paper will serve as a baseline for assessing the performance of control approaches developed in the future.
Jaroslav Klapálek, Faculty of Electrical Engineering, CTU in Prague, CZECH REPUBLIC Michal Sojka, Czech Institute of Informatics, Robotics and Cybernetics, CTU in Prague, CZECH REPUBLIC Zdeněk Hanzálek, Czech Institute of Informatics, Robotics and Cybernetics, CTU in Prague, CZECH REPUBLIC
Comparison of Control Approaches for Autonomous Race Car Model
F2020-ACM-053 • Paper + Video • FISITA World Congress 2021 • ACM - Automated and Connected Mobility
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