13 citations · 27 across the 12 of their papers we have counts for
12 papers
Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving
Ruoyu Yao, Ruiguo Zhong, Pei Liu +3
Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning and open-world generalizati…
DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
Rui Yang, Lei Zheng, Ruoyu Yao +1
Diffusion models have emerged as a powerful approach for multimodal motion planning in autonomous driving. However, their practical deployment is typically hindered by the inherent…
CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Ruiguo Zhong, Ruoyu Yao, Pei Liu +3
Accurate trajectory prediction and motion planning are crucial for autonomous driving systems to navigate safely in complex, interactive environments characterized by multimodal un…
Orchestrate, Generate, Reflect: A VLM-Based Multi-Agent Collaboration Framework for Automated Driving Policy Learning
Zengqi Peng, Yusen Xie, Yubin Wang +3
The advancement of foundation models fosters new initiatives for policy learning in achieving safe and efficient autonomous driving. However, a critical bottleneck lies in the manu…
Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers
Rui Yang, Lei Zheng, Shuzhi Sam Ge +1
Autonomous vehicles must navigate dynamically uncertain environments while balancing safety and efficiency. This challenge is exacerbated by unpredictable human-driven vehicle (HV)…
Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving
Lei Zheng, Rui Yang, Minzhe Zheng +3
Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This paper proposes an occlusion-awa…
CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model
Ruoyu Yao, Yubin Wang, Haichao Liu +4
Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two p…
Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
Lei Zheng, Rui Yang, Minzhe Zheng +2
Ensuring safety and driving consistency is a significant challenge for autonomous vehicles operating in partially observed environments. This work introduces a consistent parallel…
Incremental Bayesian Learning for Fail-Operational Control in Autonomous Driving
Lei Zheng, Rui Yang, Zengqi Peng +3
Abrupt maneuvers by surrounding vehicles (SVs) can typically lead to safety concerns and affect the task efficiency of the ego vehicle (EV), especially with model uncertainties ste…
Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving
Lei Zheng, Rui Yang, Michael Yu Wang +1
Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced…
Real-Time Parallel Trajectory Optimization with Spatiotemporal Safety Constraints for Autonomous Driving in Congested Traffic
Lei Zheng, Rui Yang, Zengqi Peng +3
Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffi…
Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic
Lei Zheng, Rui Yang, Zengqi Peng +2
In dense traffic scenarios, ensuring safety while keeping high task performance for autonomous driving is a critical challenge. To address this problem, this paper proposes a compu…