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

Betz, Johannes; Betz, Tobias; Fent, Felix; Geisslinger, Maximilian; Heilmeier, Alexander; Hermansdorfer, Leonhard; Herrmann, Thomas; Huch, Sebastian; Karle, Phillip; Lienkamp, Markus; Lohmann, Boris; Nobis, Felix; Ögretmen, Levent; Rowold, Matthias; Sauerbeck, Florian; Stahl, Timothy; Trauth, Rainer; Werner, Frederik; Wischnewski, Alexander

doi:10.48550/arxiv.2205.15979

Cited by 3 publications

(3 citation statements)

References 73 publications

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“…For the collision checks, the size of the obstacles is virtually increased to create the lateral bounds between which the used tracking controller can alter the trajectory without knowing the obstacles and still be collision-free. More details on the collision checks can be found in [40].…”

Section: Spatiotemporal Graphmentioning

confidence: 99%

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

et al. 2022

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Autonomous racing has increasingly become a research subject as it provides insights into dynamic, high-speed situations. One crucial aspect of handling these situations, especially in the presence of dynamic obstacles, is the generation of a collision-free trajectory that represents a safe behavior and is also competitive in the case of racing. We propose a local planning approach that generates such trajectories for a racing car on an oval race track by searching a spatiotemporal graph. A considerable challenge of search-based methods in a spatiotemporal domain is the curse of dimensionality. Therefore, we propose how a previously presented graph structure that is based on intervals instead of discrete values can be searched more efficiently without losing optimality by using a uniform-cost search strategy. We extend the search method to make it anytime-capable so that it can provide a suboptimal trajectory even if the search has to be terminated early. The graph-based planning approach allows us to apply a flexible cost function so that our approach can operate fully autonomously on an oval race track, including the pit lane. We present a cost function for oval racing and explain how the terms contribute to the desired behaviors. This is supported by results with a full-scale prototype.

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Section: Spatiotemporal Graphmentioning

confidence: 99%

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

et al. 2022

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“…A comprehensive overview of the current autonomous racing platforms, emphasizing the software-hardware co-evolution to the current stage, is presented in [1]. After IAC 2021 and CES 2022, each team presented its full stack of autonomous systems and approaches in [2], [3]. Moreover, the winner of the 2021 IAC, TUM Autonomous Motorsport team, is one of the pioneers of autonomous racing, sharing their state-of-the-art studies from planning [4]- [7] to optimal control systems [8], [9].…”

Section: Related Workmentioning

confidence: 99%

Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

Lee¹,

Jung²,

Finazzi³

et al. 2022

Preprint

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This paper describes a resilient navigation and planning algorithm for the high-speed Indy autonomous challenge (IAC). The IAC is a competition with full-scale autonomous race cars that drives up to 290 km/h (180 mph). However, owing to race cars' high-speed and heavy vibration, GPS/INS system is prone to degradation, causing critical localization errors and leading to serious accidents. To this end, we propose a robust navigation system to implement a multi-sensor fusion Kalman filter. We present the degradation identification based on probabilistic approaches to computing optimal measurement values for the Kalman filter correction step. Simultaneously, we present a resilient navigation system so that the race car follows the race track in the event of localization failure. In addition, an optimal path planning algorithm for obstacle avoidance is proposed. Considering the original optimal racing line, obstacles, and vehicle dynamics, we propose a roadgraph-based path planning algorithm to ensure that our race car drives in in-bounded conditions. The designed localization system was experimentally evaluated to determine its ability to handle the degraded data and prevent serious crashing accidents during high-speed driving. Finally, we describe the successful completion of the obstacle avoidance challenge.

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“…It is a sequential architecture consisting of a localization, perception, tracking, prediction, local planning and control module. We address only the local planning interfaces here and refer to [24] for more details.…”

Section: Autonomous Driving Software Architecturementioning

confidence: 99%

Smooth Trajectory Planning at the Handling Limits for Oval Racing

et al. 2022

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In motion planning for autonomous racing, the challenge arises in planning smooth trajectories close to the handling limits of the vehicle with a sufficient planning horizon. Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. However, the resulting rough trajectories cannot reach the handling limits of the vehicle and are also difficult to track by the controller, which can lead to unstable driving behavior. In this paper, we show that the initial edges connecting the vehicle’s estimated state with the actual graph are crucial for vehicle stability and race performance. We therefore propose a sampling-based approach that relies on jerk-optimal curves to generate these initial edges. The concept is introduced using a layer-based graph, but it can be applied to other graph structures as well. We describe the integration of the curves within the graph and the required adaptation to racing scenarios. Our approach enables stable driving at the handling limits and fully autonomous operation on the race track. While simulations show the comparison of our concept with an alternative approach based on uniform acceleration, we also present experimental results of a dynamic overtake with speeds up to 74 m/s on a full-size vehicle.

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

Cited by 3 publications

References 73 publications

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

Smooth Trajectory Planning at the Handling Limits for Oval Racing

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