A5026439472- Autonomous Vehicle Technology and Safety
- Traffic control and management
- Transportation Planning and Optimization
- Transportation and Mobility Innovations
- Vehicle emissions and performance
- Traffic Prediction and Management Techniques
- Advanced Battery Technologies Research
- Energy Harvesting in Wireless Networks
- Human-Automation Interaction and Safety
- Wireless Power Transfer Systems
- Scientific Computing and Data Management
- Climate Change Policy and Economics
- Traffic and Road Safety
- Electric Vehicles and Infrastructure
- Innovative Approaches in Technology and Social Development
- Modular Robots and Swarm Intelligence
- Particle accelerators and beam dynamics
- Real-time simulation and control systems
- Distributed and Parallel Computing Systems
- Particle Accelerators and Free-Electron Lasers
- Advanced DC-DC Converters
- Particle Detector Development and Performance
- Innovation, Technology, and Society
- University-Industry-Government Innovation Models
- Fault Detection and Control Systems
University of Illinois Urbana-Champaign
2024
Texas A&M University
2022-2024
Dalian Polytechnic University
2024
Institute of High Energy Physics
2024
Chinese Academy of Sciences
2024
Lanzhou University
2024
Mitchell Institute
2022
This paper initiates with an overview of the prevailing landscape green collaborative innovation, encompassing viewpoints policy-makers, enterprises, and academic research institutions. A comprehensive analysis is conducted on specific execution strategies which include policy formulation guidance, technological innovation advancement, optimization energy structures promotion multi-energy synergy, enhancement market mechanisms, as well facilitation international cooperation open knowledge...
This paper develops a novel car-following control method to reduce voluntary driver interventions and improve traffic stability in Automated Vehicles (AVs). Through combination of experimental empirical analysis, we show how can instigate substantial disturbances that are amplified along the upstream. Motivated by these findings, present framework for intervention based on evidence accumulation (EA), which describes evolution driver's distrust automation, ultimately resulting intervention....
This paper presents a data-driven framework to analyze the disturbance amplification behavior of automated vehicles in car-following (CF). The can be applied unknown CF controllers based on concept empirical frequency response function (FRF). Specifically, well-known signal processing method, Welch’s together with short time Fourier transformation is developed extract transfer functions from vehicle trajectories. method first assuming linear controller time-invariant control features...
This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging multi-scale control framework. The proposed framework consists of two-layer design: the upper level sequences vehicles to balance density difference between mainline and segments while enhancing lower-level efficiency through mixed-integer linear programming formulation. Based on this, employs longitudinal distributed model predictive controller (MPC) with virtual car-following...
This paper proposes a multi-connected and autonomous vehicle cooperative smooth LC control strategy (CS-LC) to improve traffic efficiency with guaranteed executing applicability. Specifically, hierarchical structure is applied in the proposed CS-LC on both original target lanes. It decomposes into two subsets of controllers (i.e. upper-layer low-layer). The controller first determines movement following by considering ambient conditions. lower-layer consecutively controls moving status lane...