High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility

arXiv cs.RO / 2026/3/24

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要点

  • The paper presents a local trajectory planner that enables high-speed autonomous driving on rugged, non-planar off-road terrain while maintaining safety at mobility limits.
  • It introduces an off-road vehicle dynamics model for rough terrain prediction, and formulates model predictive control (MPC) to mitigate rollover risks that existing methods often fail to handle.
  • A new energy-based constraint is used to safely enable extreme mobility behaviors, including cases involving tire liftoff without triggering rollover.
  • The approach is analytically argued to address rollover failure modes neglected by many state-of-the-art methods, and real-time feasibility is achieved via parallelized GPGPU computation.
  • The planner’s performance is validated through simulation and full-scale physical experiments, showing fewer rollovers and higher success rates than a baseline across challenging scenarios.

Abstract

A novel local trajectory planner, capable of controlling an autonomous off-road vehicle on rugged terrain at high-speed is presented. Autonomous vehicles are currently unable to safely operate off-road at high-speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real-time feasible. To address this challenge, a novel model predictive control (MPC) formulation is developed for local trajectory planning. A new dynamics model for off-road vehicles on rough, non-planar terrain is derived and used for prediction. Extreme mobility, including tire liftoff without rollover, is safely enabled through a new energy-based constraint. The formulation is analytically shown to mitigate rollover types ignored by many state-of-the-art methods, and real-time feasibility is achieved through parallelized GPGPU computation. The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full-scale physical experiments. The results demonstrate fewer rollovers and more successes compared to a state-of-the-art baseline across several challenging scenarios that push the vehicle to its mobility limits.