Racing line optimisation for an advanced driver assistance system

被引:0
|
作者
Salzmann, Falk [1 ]
Gadi, Sofiane [2 ]
Gundlach, Ingmar [3 ]
机构
[1] Univ Technol Dresden, Dept Vehicle Mechatron, Georg Bahr St 1b, D-01069 Dresden, Germany
[2] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, Automat Control Lab, Tannenstr 1, CH-8092 Zurich, Switzerland
[3] Tech Univ Darmstadt, Dept Control Syst & Mechatron, Landgraf Georg Str 4, D-64283 Darmstadt, Germany
关键词
advanced driver assistance; connected car; reinforcement learning; trajectory optimisation; vehicle dynamics; RECURRENT NEURAL-NETWORKS; IDENTIFICATION; APPROXIMATION;
D O I
10.1504/IJVP.2022.119437
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This paper deals with an accurate, robust and efficient optimisation method for time optimal path planning on circular tracks. Starting with a general description of the problem, suitable method domains for time-optimal path planning are qualified. In terms of reproducibility and accuracy, we propose an algorithm combining a model- and a policy-based method which takes car, track and driving data gathered from connected cars into account. Hence, it can provide a consistently learning as well as a sufficient constant time-optimal racing line on worldwide race tracks for different driver assistance purposes. We evaluated the calculated racing lines with respect to heuristic criteria like curve cutting behaviour and by comparing them to ones driven by professional race drivers.
引用
收藏
页码:46 / 73
页数:28
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