187 citations · 1.3k across the 43 of their papers we have counts for
43 papers
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…
Calibrating Car-Following Models via Bayesian Dynamic Regression
Chengyuan Zhang, Wenshuo Wang, Lijun Sun
Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-f…
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…
Understanding Bugs in Multi-Language Deep Learning Frameworks
Zengyang Li, Sicheng Wang, Wenshuo Wang +3
Deep learning frameworks (DLFs) have been playing an increasingly important role in this intelligence age since they act as a basic infrastructure for an increasingly wide range of…
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…
Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms
Nachuan Ma, Jiahe Fan, Wenshuo Wang +4
Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey article…
Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix
Wenshuo Wang, Chen Wu, Liang Cheng +1
The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program ca…
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…
A Power Analysis of the Conditional Randomization Test and Knockoffs
Wenshuo Wang, Lucas Janson
In many scientific problems, researchers try to relate a response variable to a set of potential explanatory variables , and start by trying to identify va…
Fuzzing Based on Function Importance by Interprocedural Control Flow Graph
Wenshuo Wang, Liang Cheng, Yang Zhang
Coverage-based graybox fuzzer (CGF), such as AFL has gained great success in vulnerability detection thanks to its ease-of-use and bug-finding power. Since some code fragments such…
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…
Decision-Making in Driver-Automation Shared Control: A Review and Perspectives
Wenshuo Wang, Xiaoxiang Na, Dongpu Cao +4
Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver's abilities to control. The human driver, a…
Stratification and Optimal Resampling for Sequential Monte Carlo
Yichao Li, Wenshuo Wang, Ke Deng +1
Sequential Monte Carlo (SMC), also known as particle filters, has been widely accepted as a powerful computational tool for making inference with dynamical systems. A key step in S…
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…
Recurrent Attentive Neural Process for Sequential Data
Shenghao Qin, Jiacheng Zhu, Jimmy Qin +2
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs. Furthermore,…
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…
Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
Qin Lin, Wenshuo Wang, Yihuan Zhang +1
Understanding multi-vehicle interactive behaviors with temporal sequential observations is crucial for autonomous vehicles to make appropriate decisions in an uncertain traffic env…
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
Rui Chen, Wenshuo Wang, Zirui Zhao +1
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assum…
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…