# Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments

arXiv: https://arxiv.org/abs/1904.02341 · HTML: https://mlsanity.com/paper/1904.02341/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Risk Conditioned Neural Motion Planning](https://mlsanity.com/paper/2108.01851/) (2021)
- [Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2312.05784/) (2023)
- [Security of Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2212.06123/) (2022)
- [Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection](https://mlsanity.com/paper/2210.01766/) (2022)
- [Dual Formulation for Chance Constrained Stochastic Shortest Path with Application to Autonomous Vehicle Behavior Planning](https://mlsanity.com/paper/2302.13115/) (2023)
- [Safe Explicable Planning](https://mlsanity.com/paper/2304.03773/) (2023)
- [A Fully Polynomial Time Approximation Scheme for Constrained MDPs and Stochastic Shortest Path under Local Transitions](https://mlsanity.com/paper/2204.04780/) (2022)
- [Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observation](https://mlsanity.com/paper/2402.18149/) (2024)
- [MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning](https://mlsanity.com/paper/2605.08512/) (2026)

## Abstract (arXiv)

A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or over-conservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.