19 citations · 74 across the 18 of their papers we have counts for
18 papers
Slow Brain, Fast Planner: Latency-Resilient VLM-Augmented Urban Navigation
Zhenghao "Mark'' Peng, Honglin He, Quanyi Li +2
Learning-based planners for sidewalk navigation can generate diverse candidate trajectories in real time, yet their scoring functions often fail to select the best trajectory in ch…
Grounded World Model: Latent Planning with Language Goals
Quanyi Li, Lan Feng, Haonan Zhang +4
World models such as DINO-WM and LeWM specify the goal with an image, which is difficult to obtain in advance for novel tasks. We present the Grounded World Model (GWM), a latent w…
RAP: 3D Rasterization Augmented End-to-End Planning
Lan Feng, Yang Gao, Eloi Zablocki +5
Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be…
VLAs are Confined yet Capable of Generalizing to Novel Instructions
Quanyi Li
Vision-language-action models (VLAs) often achieve high performance on demonstrated tasks but struggle significantly when required to extrapolate, combining skills learned from dif…
Towards Autonomous Micromobility through Scalable Urban Simulation
Wayne Wu, Honglin He, Chaoyuan Zhang +5
Micromobility, which utilizes lightweight mobile machines moving in urban public spaces, such as delivery robots and mobility scooters, emerges as a promising alternative to vehicu…
Learning from Active Human Involvement through Proxy Value Propagation
Zhenghao Peng, Wenjie Mo, Chenda Duan +2
Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from…
MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility
Wayne Wu, Honglin He, Jack He +5
Public urban spaces like streetscapes and plazas serve residents and accommodate social life in all its vibrant variations. Recent advances in Robotics and Embodied AI make public…
A Simple Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation
Pan He, Quanyi Li, Xiaoyong Yuan +1
Traffic signal control (TSC) is crucial for reducing traffic congestion leading to smoother traffic flow, reduced idle time, and mitigated CO2 emissions. In this paper, we explore…
CAT: Closed-loop Adversarial Training for Safe End-to-End Driving
Linrui Zhang, Zhenghao Peng, Quanyi Li +1
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a…
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
Quanyi Li, Zhenghao Peng, Lan Feng +4
Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and…
Guarded Policy Optimization with Imperfect Online Demonstrations
Zhenghai Xue, Zhenghao Peng, Quanyi Li +2
The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrati…
TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
Lan Feng, Quanyi Li, Zhenghao Peng +2
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called Traff…
Human-AI Shared Control via Policy Dissection
Quanyi Li, Zhenghao Peng, Haibin Wu +2
Human-AI shared control allows human to interact and collaborate with AI to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempt t…
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Quanyi Li, Zhenghao Peng, Bolei Zhou
Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given…
Safe Driving via Expert Guided Policy Optimization
Zhenghao Peng, Quanyi Li, Chunxiao Liu +1
When learning common skills like driving, beginners usually have domain experts standing by to ensure the safety of the learning process. We formulate such learning scheme under th…
Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization
Zhenghao Peng, Quanyi Li, Ka Ming Hui +2
Self-Driven Particles (SDP) describe a category of multi-agent systems common in everyday life, such as flocking birds and traffic flows. In a SDP system, each agent pursues its ow…
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Quanyi Li, Zhenghao Peng, Lan Feng +3
Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffi…
Improving the Generalization of End-to-End Driving through Procedural Generation
Quanyi Li, Zhenghao Peng, Qihang Zhang +2
Over the past few years there is a growing interest in the learning-based self driving system. To ensure safety, such systems are first developed and validated in simulators before…