Multistep Belief Space Dynamics Learning For Risk-Aware Control

2026-05-12

AI Summary

This work addresses the limited human-like risk perception of autonomous vehicles in complex environments by proposing a learning framework that integrates multi-step belief-space distribution dynamics with model predictive control (MPC). The approach employs a structured neural network to learn the evolution of uncertainty, enabling natural speed modulation and intelligent decision-making without excessive conservatism. Innovatively embedding distributional predictions directly into real-time MPC optimization significantly enhances the system’s adaptability to dynamic risks. Evaluated on a large-scale real-world off-road driving dataset and validated on a full-scale vehicle, the method demonstrates robust performance and effectiveness across diverse and challenging terrains.

Abstract

As autonomous vehicles move from a simplified research setting to practical use, there exists a large gap between the dynamic behavior of a human driving and an autonomous system. Risk-aware behavior needs to naturally develop in order to scale to the demands of the real world. A major issue for risk-aware planning and control has been predicting how dynamical uncertainty evolves through time and optimizing plans that account for this without being overly conservative. Here, we present a learning framework to predict distributional dynamics that can be optimized in real time for Model Predictive Control (MPC). We explore the importance of structure when learning distributional dynamics for use in MPC. A rigorous ablation study is conducted on a large dataset of real world off-road driving that shows the impact of deviations from our proposed structure. Furthermore, we deploy our learned model and planning stack on a full sized vehicle in challenging off-road conditions. Our planning architecture is able to naturally regulate the speed of the vehicle based on the environment and consistently demonstrates intelligent behavior over miles of diverse terrain.

Problem

• risk-aware control
• dynamical uncertainty
• belief space dynamics
• autonomous vehicles
• model predictive control

Innovation

• belief space dynamics
• risk-aware control
• distributional dynamics learning
• model predictive control
• structured learning