Search Results for author: Zhenyu Shou

Found 6 papers, 0 papers with code

Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study

no code implementations7 Dec 2021 Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen

To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions.

Unity

Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment

no code implementations22 Nov 2020 Zhenyu Shou, Xu Chen, Yongjie Fu, Xuan Di

We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium (DUE) when traffic environments are simulated using dynamic loading models (DNL).

Autonomous Vehicles Bilevel Optimization +2

Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles

no code implementations8 Jul 2020 Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen

With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the transportation system.

Position Sensor Fusion

Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron

no code implementations23 Jun 2020 Zhenyu Shou, Ziran Wang, Kyungtae Han, Yongkang Liu, Prashant Tiwari, Xuan Di

Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment.

Autonomous Driving

Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning

no code implementations17 Feb 2020 Zhenyu Shou, Xuan Di

In the second case study, an optimal toll charge of $5. 1 is solved using BO, which improves the objective of city planners by 7. 9%, compared to that without any toll charge.

Bayesian Optimization Bilevel Optimization +3

Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning

no code implementations23 May 2019 Zhenyu Shou, Xuan Di, Jieping Ye, Hongtu Zhu, Hua Zhang, Robert Hampshire

Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment.

Imitation Learning

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