Stability Control for Real World Testing in Autonomous Racing
Authors: Phillip Pitschi, Simon Sagmeister, Frederik Werner, Markus Lienkamp, Boris Lohmann
Organizations: Technical University of Munich, Germany; School of Engineering & Design, Department of Engineering Physics and Computation, Institute of Automatic Control · Technical University of Munich, Germany; School of Engineering & Design, Department of Mobility Systems Engineering, Institute of Automotive Technology
Controlling an autonomous vehicle at the limits of handling is a challenging task. Due to external influences, such as road conditions or weather, a vehicle can easily become unstable. Since most control algorithms assume stable vehicle behavior, they might fail in these situations. Especially when operating expensive vehicles without a safety driver on board, as in autonomous racing, this poses a significant challenge. To enable safe operation at the vehicle's dynamic limits, we present a comprehensive stability control system that safeguards motion control algorithms in autonomous driving. The proposed system consists of an electronic stability control (ESC), a slip control (SC), and a countersteer system (CS), which collectively adapt steering and brake commands from the motion controller to maintain vehicle stability. We validate our approach through both simulation and experiments on a real-world, full-scale vehicle. The results show that the stability control system maintains vehicle stability in critical situations and extends the operational feasible region. To simplify integration, we provide an open-source implementation at github.com/TUMFTM/tam-stability-control.
Figures & tables
Fig. 1: Dallara EAV25 race car of the TUM Autonomous Motorsport team at Yas Marina Circuit in Abu Dhabi.
Fig. 2: Stability control system architecture.
Fig. 3: Structure of the esc system.
Fig. 4: Schematic of the sc finite state machine. sc influences the brake pressure rate ϵ . State transitions depend on wheel slip λ and its derivative λ˙ .
Fig. 5: Countersteer system concept. The grey trajectory represents the nominal case without longitudinal load or disturbance, while the blue trajectory illustrates an oversteer situation. For both situations, the instantaneous center of rotation (ICR) is equidistant from the vehicle’s center of gravity (COG). As a result, the driven radius and the yaw rate remain identical.
Fig. 6: Simulative comparison of active and inactive sc for increasing acceleration scaling values. Solid lines correspond to the front axle, dashed lines to the rear axle.
Fig. 7: Simulative comparison of different stability control configurations for increasing acceleration scaling values.
Fig. 8: Simulative comparison of different stability control configurations for the first lap at an acceleration scaling value of GG=1.4 .
Fig. 9: Simulative comparison of stability control for different motion controllers for increasing acceleration scaling values. Solid lines correspond to the case without stability control, dashed lines correspond to the case with stability control using both esc and cs .
Fig. 10: Countersteer intervention during a real world test. The semi-transparent lines correspond to the slip angles on the left and right side of the vehicle, while the solid line corresponds to the weighted slip angle.
Fig. 11: esc intervention during a real world test.
Fig. 12: Countersteer intervention during fastest lap of testing on the Yas Marina Circuit.
College of Automotive and Energy Engineering, Tongji University, Shanghai, 201804, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore