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

• URL: http://arxiv.org/abs/2603.20525v1
• Date: Fri, 20 Mar 2026 21:51:10 GMT
• Title: High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility
• Abstract summary: A novel model predictive control (MPC) formulation is developed for local trajectory planning.<n>MPC is analytically shown to mitigate rollover types ignored by many state-of-the-art methods.<n>The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full-scale physical experiments.
• Score: 5.779535973286779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning [57.68768189557651]
  In Formula 1, drivers optimize racing lines within tire grip limits to minimize lap times.<n>In rally racing, drivers intentionally break traction to drift on loose surfaces.<n>This paper proposes a planning-control framework specifically designed for MLT drifting scenario.
  arXiv  Detail & Related papers  (2026-07-31T08:50:40Z)
• nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks [16.180911702540836]
  This letter presents the first high-fidelity benchmark, called nuTruck, designed to support large-scale neural network training and closed-loop evaluation.<n>We first incorporate a highly accurate nonlinear truck dynamical model into the simulation, which enables independent driving and steering of all wheels.<n>Second, we adapt several rule-based and learning-based planners as baselines for DETs and evaluate their performance in closed-loop simulation.
  arXiv  Detail & Related papers  (2026-07-15T11:09:48Z)
• Learning When to Jump for Off-road Navigation [10.997518969388084]
  We introduce Motion-aware Traversability representation to explicitly model terrain cost conditioned on actual robot motion.<n>We develop a system that integrates MAT to enable agile off-road navigation.<n>Results show that MAT achieves real-time efficiency and enhances the performance of off-road navigation.
  arXiv  Detail & Related papers  (2026-01-31T19:41:09Z)
• Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field [0.0]
  The paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace.<n>It proposes a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF)<n>In this approach, a bounded weighting function, $(,v_e)$, is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle.
  arXiv  Detail & Related papers  (2025-12-08T14:57:01Z)
• Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving [11.436186697804835]
  This paper proposes a novel framework that formulates trajectory prediction as a constrained regression guided by permissible driving directions and their boundaries.<n>We evaluate our approach on the Argoverse-2 dataset against the HPTR baseline.
  arXiv  Detail & Related papers  (2025-05-10T19:21:00Z)
• A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses [68.68514648185828]
  Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles.<n>Current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements.<n>This study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss.
  arXiv  Detail & Related papers  (2024-11-29T14:47:08Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control [4.23755398158039]
  We propose a method for learning terrain-aware kinodynamic model conditioned on both proprioceptive and exteroceptive information. The proposed model generates reliable predictions of 6-degree-of-freedom motion and can even estimate contact interactions. We demonstrate the effectiveness of our approach through experiments on a simulated off-road track, showing that our proposed model-controller pair outperforms the baseline.
  arXiv  Detail & Related papers  (2023-05-01T06:09:49Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)

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.