Xiao ZHOU

Xiao ZHOU

She/Her

Multi-agent system, Sequential Decision Making, Autonomous Driving, Robotic Perception

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Game-Theoretic Driver Modeling and Decision-Making for Autonomous Driving with Temporal-Spatial Attention-Based Deep Q-Learning

Published in IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2024

A game-theoretic driver modeling and decision-making framework with level-k reasoning for autonomous vehicles is proposed. A temporal-spatial attention-based deep Q-learning (TSA-DQN) algorithm is developed to estimate the decision level of surrounding vehicles and optimize ego vehicle’s decision. Simulations demonstrate improved safety, efficiency, and success rates over baselines in various driving scenarios. Real-world testing further confirms the algorithm’s practical feasibility.

Conference Papers

Integrated Intention Prediction and Decision-Making with Spectrum Attention Net and Proximal Policy Optimization

Published in The 27th IEEE International Conference on Intelligent Transportation Systems. 24 Sep- 27 Sep 2024 | Edmonton, Canada, 2024

This paper presents an integrated intention prediction and decision-making framework for autonomous driving. A spectrum attention net predicts surrounding vehicle intentions efficiently by analyzing frequency components, while the proximal policy optimization (PPO) algorithm addresses non-stationary issues with policy updates. Joint learning integrates both modules, and experiments demonstrate superior performance in safety, efficiency, and success rates compared to deep reinforcement learning baselines.

Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections

Published in International Conference on Intelligent Robots and Systems (IROS) · IROS 2024. 14 October- 18 October 2024 | Abu Dhabi, UAE, 2024

This paper introduces a reward-driven automated curriculum learning framework for self-driving at unsignalized intersections, addressing uncertainties in surrounding vehicles’ intentions and quantities. A progressive curriculum and well-designed reward function improve policy exploration and sample efficiency. Experiments in Highway-Env and CARLA show superior success rates, robustness, and adaptability, validating the method’s effectiveness in handling complex driving interactions.

Putn: A plane-fitting based uneven terrain navigation framework

Published in International Conference on Intelligent Robots and Systems (IROS) · IROS 2022. 23 October- 27 October 2022 | Kyoto, Japan, 2022

This paper proposes a Plane-Fitting based Uneven Terrain Navigation (PUTN) framework for autonomous ground robots in unstructured 3D environments. PUTN combines an improved Plane Fitting RRT* for sparse trajectory generation, Gaussian Process Regression for trajectory interpolation, and Nonlinear Model Predictive Control (NMPC) for local planning. Experiments demonstrate PUTN’s robust, safe motion planning in uneven terrain, validated through real-world testing.