# Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach

arXiv: https://arxiv.org/abs/2409.13993 · HTML: https://mlsanity.com/paper/2409.13993/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 25 of its 60 references that are in the map.
- First posted on arXiv 21 Sep 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 25 of 25)

- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Search-based optimal motion planning for automated driving](https://mlsanity.com/paper/1803.04868/) (2018)
- [Stochastic Dynamic Games in Belief Space](https://mlsanity.com/paper/1909.06963/) (2019)
- [EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments](https://mlsanity.com/paper/2108.07993/) (2021)
- [Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions](https://mlsanity.com/paper/2110.01027/) (2021)
- [GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging](https://mlsanity.com/paper/2109.01896/) (2021)
- [Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving](https://mlsanity.com/paper/2207.03673/) (2022)
- [Contingency Games for Multi-Agent Interaction](https://mlsanity.com/paper/2304.05483/) (2023)
- [Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving](https://mlsanity.com/paper/2011.00462/) (2020)
- [Cooperative Decision Making of Connected Automated Vehicles at Multi-lane Merging Zone: A Coalitional Game Approach](https://mlsanity.com/paper/2103.07887/) (2021)
- [Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM](https://mlsanity.com/paper/2301.04386/) (2023)
- [RACP: Risk-Aware Contingency Planning with Multi-Modal Predictions](https://mlsanity.com/paper/2402.17387/) (2024)
- [Learning to Correlate in Multi-Player General-Sum Sequential Games](https://mlsanity.com/paper/1910.06228/) (2019)
- [ALGAMES: A Fast Solver for Constrained Dynamic Games](https://mlsanity.com/paper/1910.09713/) (2019)
- [Potential Game-Based Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2201.06157/) (2022)
- [MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving](https://mlsanity.com/paper/2308.12021/) (2023)
- [Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching](https://mlsanity.com/paper/2003.02746/) (2020)
- [Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories](https://mlsanity.com/paper/2107.04926/) (2021)
- [Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits](https://mlsanity.com/paper/2201.01428/) (2022)
- [Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours](https://mlsanity.com/paper/2103.07910/) (2021)
- [How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games](https://mlsanity.com/paper/2210.13064/) (2022)

## Built on by (most cited first, 2 of 2)

- [Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles](https://mlsanity.com/paper/2412.01234/) (2024)
- [Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving](https://mlsanity.com/paper/2606.03296/) (2026)

## Abstract (arXiv)

Modeling the interaction between traffic agents is a key issue in designing safe and non-conservative maneuvers in autonomous driving. This problem can be challenging when multi-modality and behavioral uncertainties are engaged. Existing methods either fail to plan interactively or consider unimodal behaviors that could lead to catastrophic results. In this paper, we introduce an integrated decision-making and trajectory planning framework based on Bayesian game (i.e., game of incomplete information). Human decisions inherently exhibit discrete characteristics and therefore are modeled as types of players in the game. A general solver based on no-regret learning is introduced to obtain a corresponding Bayesian Coarse Correlated Equilibrium, which captures the interaction between traffic agents in the multimodal context. With the attained equilibrium, decision-making and trajectory planning are performed simultaneously, and the resulting interactive strategy is shown to be optimal over the expectation of rivals' driving intentions. Closed-loop simulations on different traffic scenarios are performed to illustrate the generalizability and the effectiveness of the proposed framework.