Stability Control for Real World Testing in Autonomous Racing
- URL: http://arxiv.org/abs/2608.17779v1
- Date: Tue, 18 Aug 2026 13:41:56 GMT
- Title: Stability Control for Real World Testing in Autonomous Racing
- Abstract summary: We present a comprehensive stability control system that safeguards motion control algorithms in autonomous driving.<n>The proposed system consists of an electronic stability control (ESC), a slip control (SC), and a countersteer system (CS)<n>We validate our approach through both simulation and experiments on a real-world, full-scale vehicle.
- Score: 4.542902590888213
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Emergent Autonomous Drifting for Collision Avoidance in Real-World Winter Driving Scenarios [0.3899855581265356]
We present a drift-capable nonlinear model predictive control (MPC) system designed to handle scenarios grounded in crash fatality data.<n>The controller naturally initiates and sustains drifting maneuvers to stay on the road when hitting a patch of ice on the rear axle.<n>A Monte Carlo study over random ice patches shows that the drift-capable controller achieves lower median lane error than ESC across several speeds.
arXiv Detail & Related papers (2026-07-21T18:05:19Z) - Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems [6.355446464457206]
This paper presents the controller framework, the model extensions, and real-world experimental results.<n>We observe that our controller enables a production sports car with a combustion engine to robustly sustain circular and figure-eight drifts, limiting lateral error to 1.1 m and sideslip overshoot to 0.06 rad despite actuator delays exceeding 250 ms.<n>In conclusion, our results establish that autonomous drifting is feasible on production-ready vehicles, opening pathways to advanced safety systems capable of stabilizing cars in scenarios where traditional control fails.
arXiv Detail & Related papers (2026-06-26T10:05:30Z) - Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations [122.22391796628408]
We present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving.<n>We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures, ego-state estimation errors, and compute-induced control delay.<n>Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance.
arXiv Detail & Related papers (2026-05-18T08:45:24Z) - High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety [0.0]
Bicyclists present unique characteristics that make their safety both critical and also manageable.<n>Vehicles often travel at significantly higher relative speeds when interacting with bicyclists.<n>This study proposes and develops a High Order Control Lyapunov Function High Order Control Barrier Function Quadratic Programming (HOCLF HOCBF QP) control framework.
arXiv Detail & Related papers (2025-12-14T17:29:34Z) - Learning-based Autonomous Oversteer Control and Collision Avoidance [5.523708201910051]
Oversteer, wherein a vehicle's rear tires lose traction and induce unintentional excessive yaw, poses critical safety challenges.<n>This paper introduces a novel end-to-end (E2E) autonomous driving approach that tackles oversteer control and collision avoidance simultaneously.<n>We present Q-Compared Soft Actor-Critic (QC-SAC), a new HL algorithm that effectively learns from suboptimal demonstration data and adapts rapidly to new conditions.
arXiv Detail & Related papers (2025-05-21T08:53:38Z) - Learning Stability Attention in Vision-based End-to-end Driving Policies [100.57791628642624]
We propose to leverage control Lyapunov functions (CLFs) to equip end-to-end vision-based policies with stability properties.
We present an uncertainty propagation technique that is tightly integrated into att-CLFs.
arXiv Detail & Related papers (2023-04-05T20:31:10Z) - Differentiable Control Barrier Functions for Vision-based End-to-End
Autonomous Driving [100.57791628642624]
We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving.
We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
arXiv Detail & Related papers (2022-03-04T16:14:33Z) - Pointwise Feasibility of Gaussian Process-based Safety-Critical Control
under Model Uncertainty [77.18483084440182]
Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively.
We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
arXiv Detail & Related papers (2021-06-13T23:08:49Z) - Learning Robust Hybrid Control Barrier Functions for Uncertain Systems [68.30783663518821]
We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety.
Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data.
Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
arXiv Detail & Related papers (2021-01-16T17:53:35Z) - Learning Stabilizing Controllers for Unstable Linear Quadratic
Regulators from a Single Trajectory [85.29718245299341]
We study linear controllers under quadratic costs model also known as linear quadratic regulators (LQR)
We present two different semi-definite programs (SDP) which results in a controller that stabilizes all systems within an ellipsoid uncertainty set.
We propose an efficient data dependent algorithm -- textsceXploration -- that with high probability quickly identifies a stabilizing controller.
arXiv Detail & Related papers (2020-06-19T08:58:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.