Perception Learning, Prediction and Motion Planning for Energy Efficient Driving of Connected and Automated Vehicles

The uninterrupted growth in transportation activities has been exerting significant pressure on our socio-economics and environment in recent years. However, emerging technologies such as connected and automated vehicles (CAVs), transportation electrification, and edge computing have been stimulating increasingly dedicated efforts by engineers, researchers and policymakers to tackle these transportation-related problems, including those that are focused on energy and the environment. With the advancement towards vehicle connectivity and automation, vehicles can reduce the energy consumption, emissions and improve urban mobility and safety through environmentally-friendly eco-driving strategies, vehicle electrification, and driving coordination.

In this dissertation, we developed predictive models and trajectory planning algorithms using machine learning and optimization techniques to address four key challenge: 1) Driving in real-world scenarios with constrains and interaction from downstream vehicle's trajectory and traffic; 2) Extracting essential traffic information from sparse vehicle trajectory data in a connected vehicle environment; 3) Evaluating and quantifying the behavior of a complex Vehicle-Powertrain Eco-Operation System; and 4) Optimal scheduling and coordinating automated vehicles in terms of mobility benefits and energy savings considering the tradeoff between solution optimality and computational efficiency for online performance.