Friction uncertainty is a significant challenge for autonomous vehicle control, undermining feasibility and performance. This paper proposes a novel two-layer control framework to achieve vehicle motion control under large friction uncertainty by combining optimization-based planning with indirect adaptive robust control (IARC). The upper layer solves a constrained optimal control problem to generate a feasible trajectory that satisfies all the constraints in a time-optimal manner along the desired path. The lower layer, IARC, adapts the unknown parameters related to friction uncertainty and combines model compensation with robust feedback to ensure transient and steady-state tracking performance. An estimation monitor is designed to prevent poor learning when data indicate a model mismatch, while a smooth parameter reset reduces the estimated tire capacity and limits abrupt tightening of constraints. The updated parameters are fed back into the model and constraints in the planner, enabling trajectory replanning as friction conditions change. Simulation results indicate that the proposed method achieves accurate tracking of the desired trajectory and online friction adaptation under large friction uncertainty while ensuring time-optimal vehicle motion. The simulation results demos are available at https://github.com/TedZhou0603/CO-IARC.
Figures & tables
Fig. 1: Two-layer Control Framework.
Method
time/lap (s)
elat (m)
espeed (mm/s)
eyaw (deg)
RMSE
max
RMSE
max
RMSE
max
DRY
crashed ( s=200 m)
≥6
≥6
3.103
8.011
3.521
22.747
WET
crashed ( s=262 m)
≥6
≥6
2.161
11.721
6.254
21.021
ICE
202.7
0.213
0.779
0.203
0.735
1.418
5.467
Ours
153.2
0.208
0.772
0.168
0.688
1.224
5.389
TABLE I: Performance on Test Track with Different Friction Patches.
Fig. 2: Lateral error, speed, and yaw-angle error for the proposed method and the baseline NMPC with different prior estimates of road friction. Our method achieves the best tracking performance and time optimality compared with a high-prior NMPC that crashed under low-friction conditions, and is faster than the most conservative prior.
Fig. 3: Estimated and true rear tire capacities. The gray dashed curve gives the capacity based on the static rear load. Green and pink shading indicate driving and braking; vertical dashed lines mark friction transitions.
Robust control designs feedback that maintains stability, safety, and performance despite disturbances, uncertainty, model errors, and changing conditions.
Accurate estimation of the tire-road friction coefficient (TRFC) is critical for ensuring safe vehicle control, especially under adverse road conditions. However, most existing methods rely on naturalistic driving data from regular vehicles, which typically operate under mild acceleration and braking. As a result, the data provide insufficient slip excitation and offer limited observability of the peak TRFC. This paper presents a high-slip-ratio control framework that enables automated vehicles (AVs) to actively excite the peak friction region during empty-haul operations while maintaining operational safety. A simplified Magic Formula tire model is adopted to represent nonlinear slip-force dynamics and is locally fitted using repeated high-slip measurements. To support safe execution in car-following scenarios, we formulate a constrained optimal control strategy that balances slip excitation, trajectory tracking, and collision avoidance. In parallel, a binning-based statistical projection method is introduced to robustly estimate peak TRFC under noise and local sparsity. The framework is validated through both closed-loop simulations and real-vehicle experiments, demonstrating its accuracy, safety, and feasibility for scalable, cost-effective roadway friction screening.
Zhaohui Liang, Hang Zhou, Heye Huanh +1
Civil & Environmental Engineering Department, University of Wisconsin, Madison, Madison, WI
Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume perfect tracking and remain blind to execution errors. To guarantee safety, trajectory planners therefore restrict themselves to conservative spatial margins, leaving usable track space untapped. To overcome these issues, we introduce a control-informed online trajectory planning framework that learns from its own execution errors. By measuring systematic tracking deviations during runtime, we dynamically adapt spatial track constraints and iteratively expand the free-space planning area. The planner remains time-optimal while compensating for accumulated execution errors. This method was analyzed in a high-fidelity closed-loop simulation environment with autonomous racecars. The results demonstrate that our approach reduces lap time by 1.8,s without increasing computational burden, maintaining a median runtime of 25 ms. Our finding indicates that feeding control-induced deviations back into the planning layer unlocks performance previously inaccessible to modular architectures and enables autonomous vehicles to exploit track limits systematically.
Ann-Kathrin Schwehn, Alexander Langmann, Mattia Piccinini +1
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI)
As autonomous vehicles (AVs) operate in increasingly dynamic traffic conditions, lateral control must be performed while longitudinal speed and acceleration vary. Yet many existing lateral controllers rely on constant-speed or operating-point-based assumptions, which can degrade performance during transient longitudinal maneuvers. Moreover, most methods assume precisely known vehicle parameters, despite real-world parametric uncertainties. To address these limitations, this paper presents a longitudinal-motion-aware robust nonlinear lateral control framework for AVs. It first derives a tracking error model that depends on varying longitudinal speed and acceleration. Using this model, feedback linearization is employed to obtain a linear input-output relation for lateral error tracking while embedding longitudinal motion into the control law. The resulting internal dynamics are then analyzed to ensure overall system stability. To address parameter uncertainty, two robust control designs with distinct implementation trade-offs are proposed: (i) a Lyapunov redesign (LR) approach inspired by sliding mode control, and (ii) an incremental nonlinear dynamic inversion (INDI) method. Both are rigorously analyzed and proven to ensure ultimate boundedness, with key robustness-tuning parameters explicitly identified. Simulations demonstrate enhanced tracking accuracy, consistent performance across varying speeds and accelerations, and robustness to model uncertainties, while also examining the effects of the robustness-related parameters. Real-vehicle tests further confirm real-time implementation and practical path-tracking performance on actual hardware.
Sixu Li, Nitesh Kumar, Reyshwanth Ganeshan +3
Texas A&M University, College Station, TX 77843, USA