83 citations · 116 across the 9 of their papers we have counts for
10 papers
Robustness and Resilience Evaluation of Eco-Driving Strategies at Signalized Intersections
Zhaohui Liang, Chengyuan Ma, Keke Long +1
Eco-driving strategies have demonstrated substantial potential for improving energy efficiency and reducing emissions, especially at signalized intersections. However, evaluations…
Real-Time Lane-Level Crash Detection on Freeways Using Sparse Telematics Data
Shixiao Liang, Chengyuan Ma, Pei Li +7
Real-time traffic crash detection is critical in intelligent transportation systems because traditional crash notifications often suffer delays and lack specific, lane-level locati…
SRA-CP: Spontaneous Risk-Aware Selective Cooperative Perception
Jiaxi Liu, Chengyuan Ma, Hang Zhou +5
Cooperative perception (CP) offers significant potential to overcome the limitations of single-vehicle sensing by enabling information sharing among connected vehicles (CVs). Howev…
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
Hangyu Li, Bofeng Cao, Zhaohui Liang +16
Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenar…
Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights
Zheng Li, Peng Zhang, Shixiao Liang +5
Understanding how Advanced Driver-Assistance Systems (ADAS) interact with Traffic Control Devices (TCDs) is critical for assessing their influence on traffic operations, yet this i…
Towards Full-Scenario Safety Evaluation of Automated Vehicles: A Volume-Based Method
Hang Zhou, Chengyuan Ma, Shiyu Shen +2
With the rapid development of automated vehicles (AVs) in recent years, commercially available AVs are increasingly demonstrating high-level automation capabilities. However, most…
Assessing Markov Property in Driving Behaviors: Insights from Statistical Tests
Zheng Li, Haoming Meng, Chengyuan Ma +2
The Markov property serves as a foundational assumption in most existing work on vehicle driving behavior, positing that future states depend solely on the current state, not the s…
Goal-based Neural Physics Vehicle Trajectory Prediction Model
Rui Gan, Haotian Shi, Pei Li +6
Vehicle trajectory prediction plays a vital role in intelligent transportation systems and autonomous driving, as it significantly affects vehicle behavior planning and control, th…
Modular Vehicle Routing Problem: Applications in Logistics
Hang Zhou, Yang Li, Chengyuan Ma +2
Recent studies and industry advancements indicate that modular vehicles (MVs) have the potential to enhance transportation systems through their ability to dock and split during a…
HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
Zilin Huang, Zihao Sheng, Chengyuan Ma +1
Despite significant progress in autonomous vehicles (AVs), the development of driving policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully…