# Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions

arXiv: https://arxiv.org/abs/2212.11819 · HTML: https://mlsanity.com/paper/2212.11819/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2022.

## Built on by (most cited first, 7 of 7)

- [Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space](https://mlsanity.com/paper/2405.13969/) (2024)
- [AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics](https://mlsanity.com/paper/2505.00322/) (2025)
- [Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions](https://mlsanity.com/paper/2310.20561/) (2023)
- [Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation](https://mlsanity.com/paper/2404.01746/) (2024)
- [A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems](https://mlsanity.com/paper/2408.12187/) (2024)
- [Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics](https://mlsanity.com/paper/2408.12609/) (2024)
- [A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving](https://mlsanity.com/paper/2606.28384/) (2026)

## Abstract (arXiv)

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.