What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mechanical engineering
Control engineering, Vehicle dynamics, Control theory, Energy management and Powertrain are his primary areas of study.
The Control engineering study combines topics in areas such as Obstacle avoidance, Motion planning, Trajectory and Automation.
His Vehicle dynamics research includes themes of Control system, Control theory, Platoon and Torque.
His is doing research in Robustness and Feed forward, both of which are found in Control theory.
His studies in Energy management integrate themes in fields like Intelligent transportation system, Automotive engineering, Internal combustion engine, Optimal control and Advanced driver assistance systems.
Dongpu Cao has included themes like Electric vehicle and Automotive industry in his Powertrain study.
His most cited work include:
- Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective (326 citations)
- Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies (229 citations)
- Editors’ perspectives: road vehicle suspension design, dynamics, and control (208 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Control theory, Automotive engineering, Vehicle dynamics and Control engineering.
His work deals with themes such as Machine learning, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
His research in Control theory intersects with topics in Electric vehicle and Model predictive control.
His Automotive engineering research incorporates themes from Powertrain and Energy management.
His Vehicle dynamics research is multidisciplinary, incorporating elements of Control system and Nonlinear system.
His Control engineering research incorporates elements of Automation, Control and Reinforcement learning.
He most often published in these fields:
- Artificial intelligence (18.95%)
- Control theory (16.84%)
- Automotive engineering (15.44%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (18.95%)
- Control (12.98%)
- Reinforcement learning (7.37%)
In recent papers he was focusing on the following fields of study:
Dongpu Cao mainly investigates Artificial intelligence, Control, Reinforcement learning, Deep learning and Computer vision.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
His study looks at the intersection of Control and topics like Operations research with Trajectory.
His Reinforcement learning research includes elements of Intersection, Control theory, Real-time computing, Overtaking and Task analysis.
His work carried out in the field of Deep learning brings together such families of science as Artificial neural network, Recurrent neural network and State.
Dongpu Cao combines subjects such as Visualization and Odometry with his study of Computer vision.
Between 2019 and 2021, his most popular works were:
- A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach (45 citations)
- Personalized Vehicle Trajectory Prediction Based on Joint Time-Series Modeling for Connected Vehicles (23 citations)
- A Review of Research on Traffic Conflicts Based on Intelligent Vehicles (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Mechanical engineering
His primary areas of study are Artificial intelligence, Control, Deep learning, Reinforcement learning and Trajectory.
His Artificial intelligence study combines topics in areas such as Computer vision and Pattern recognition.
His Control research is multidisciplinary, relying on both Advanced driver assistance systems, Key and Operations research.
His Advanced driver assistance systems research is multidisciplinary, incorporating perspectives in Control system and Automation.
He works mostly in the field of Trajectory, limiting it down to concerns involving Motion planning and, occasionally, Tracking, CarSim and Obstacle avoidance.
Within one scientific family, Dongpu Cao focuses on topics pertaining to Shortest path problem under Collision avoidance, and may sometimes address concerns connected to Control theory.
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