187 citations · 1.3k across the 43 of their papers we have counts for
15 papers · 1 filter
Interactive Car-Following: Matters but NOT Always
Chengyuan Zhang, Rui Chen, Jiacheng Zhu +3
Following a leading vehicle is a daily but challenging task because it requires adapting to various traffic conditions and the leading vehicle's behaviors. However, the question `D…
Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
Letian Wang, Jie Liu, Hao Shao +4
When autonomous vehicles are deployed on public roads, they will encounter countless and diverse driving situations. Many manually designed driving policies are difficult to scale…
On Trustworthy Decision-Making Process of Human Drivers from the View of Perceptual Uncertainty Reduction
Huanjie Wang, Haibin Liu, Wenshuo Wang +1
Humans are experts in making decisions for challenging driving tasks with uncertainties. Many efforts have been made to model the decision-making process of human drivers at the be…
Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills
Tong Zhou, Letian Wang, Ruobing Chen +2
Efficient and effective exploration in continuous space is a central problem in applying reinforcement learning (RL) to autonomous driving. Skills learned from expert demonstration…
Social Interactions for Autonomous Driving: A Review and Perspectives
Wenshuo Wang, Letian Wang, Chengyuan Zhang +2
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve their goals in social traffic scenes. A rational human driver can interact with other road…
Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
Yidong Du, Wenshuo Wang, Zhigang Wang +4
The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Currently, battery disassembly is still primarily done by humans, p…
Uncovering Interpretable Internal States of Merging Tasks at Highway On-Ramps for Autonomous Driving Decision-Making
Huanjie Wang, Wenshuo Wang, Shihua Yuan +1
Humans make daily routine decisions based on their internal states in intricate interaction scenarios. This paper presents a probabilistically reconstructive learning approach to i…
On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic
Huanjie Wang, Wenshuo Wang, Shihua Yuan +2
Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring…
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang +1
Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowled…
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
Jiacheng Zhu, Shenghao Qin, Wenshuo Wang +1
Predicting surrounding vehicle behaviors are critical to autonomous vehicles when negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious train…
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
Weiyang Zhang, Wenshuo Wang, Ding Zhao
Generating multi-vehicle interaction scenarios can benefit motion planning and decision making of autonomous vehicles when on-road data is insufficient. This paper presents an effi…
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang +1
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper de…
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
Yaohui Guo, Vinay Varma Kalidindi, Mansur Arief +4
Autonomous vehicles are expected to navigate in complex traffic scenarios with multiple surrounding vehicles. The correlations between road users vary over time, the degree of whic…
Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories
Wenshuo Wang, Aditya Ramesh, Ding Zhao
Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide ran…
An Optimal LiDAR Configuration Approach for Self-Driving Cars
Shenyu Mou, Yan Chang, Wenshuo Wang +1
LiDARs plays an important role in self-driving cars and its configuration such as the location placement for each LiDAR can influence object detection performance. This paper aims…