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

2026-03-20

AI Summary

This work addresses the challenge of simultaneously achieving high-speed navigation and rollover safety in complex off-road environments, where existing autonomous driving systems often lack real-time, highly maneuverable trajectory planning capabilities. The authors propose a novel local trajectory planner based on model predictive control (MPC) that integrates an accurate vehicle dynamics model tailored for non-planar terrain with an energy-based constraint mechanism. This formulation effectively mitigates rollover risks induced by extreme events such as wheel lift-off. Real-time performance is ensured through GPGPU-accelerated parallel computation. Both theoretical analysis and experimental validation demonstrate that the proposed approach significantly reduces rollover incidents and enhances mission success rates in extreme off-road scenarios, outperforming state-of-the-art methods.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• autonomous off-road vehicle
• high-speed mobility
• rollover mitigation
• rough terrain
• real-time feasibility

Innovation

• model predictive control
• off-road autonomy
• rollover mitigation
• energy-based constraint
• GPGPU acceleration