Smooth Trajectory Planning at the Handling Limits for Oval Racing

Abstract

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.

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

“…The final trajectory should reach a minimal planning horizon of 5 s. The software architecture met all requirements listed in Section 2.1 with the tracking controller in [45], which was based on a Tube-MPC approach, and the racing line generation in [13]. The sampling procedure used to generate the initial edges is described in [46]. Similar to [24,25], it was based on the generation of jerk-minimal trajectories but only constituted the first part of the final trajectory.…”

“…The acceleration profile in Figure 6 shows that the vehicle accelerated until layer L 3 and maintained the velocity from layer L 3 to L 4 . Since the approaches in [24,25,46] sampled time, velocity, and lateral displacement for only one step, the resulting trajectories could not represent the described maneuver. However, a drawback of our approach compared to the jerk-minimal one-step approaches arises from the constant acceleration between two layers.…”

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

“…Since an additional sampling of the end acceleration would increase the computation time too much, we use an iterative process to determine only one acceleration end condition for each spatial node and end velocity combination. A detailed description of the STPS and its impact on the overall race performance and safety can be found in Ögretmen et al, 2022.…”