Date of Award
Master of Science (MS)
Connected Autonomous Vehicles are equipped with the capabilities of autonomous navigation, Vehicle to Vehicle, and Vehicle to Infrastructure communication, which have the potential to improve fuel and/ or energy efficiency. Velocity optimization is a driving technique that aims to follow a velocity profile that minimizes fuel consumption, energy consumption, idling at traffic lights, and overall trip time. Velocity optimization can be implemented in CAVs by utilizing V2I and V2V capabilities, and optimal control techniques.As CAVs become more ubiquitous, they are likely to interact closely with human driven cars. In such a scenario, it is important to find the right trade-off between safety and efficiency, as safety constraints may restrict efficient actions and vice-versa. Vehicle control systems that are heavily biased towards efficiency, may result in conservativeness and rear-ending effects in CAVs, rendering their behavior unpredictable for human drivers, which may result in collisions, compromise safety and obstruct the surrounding traffic. Through this research, we have proposed a velocity optimization strategy that optimizes the velocity profile for fuel consumption, without significantly compromising safety and affecting the traffic flow. A Model Predictive Controller is designed to compute the optimal velocity profile based on fuel consumption and impact to the surrounding traffic. A mathematical control parameter is introduced for deterministic control of impact on traffic flow. An iterative convex optimization approach is adopted for online solution of the optimal control problem. A simulation case study is presented to demonstrate fuel saving capability and reduced impact on the surrounding traffic flow, of the proposed control system.
Sathe, Sumedh, "Velocity Optimization in Connected Autonomous Vehicles and its Impact on Surrounding Traffic" (2021). All Theses. 3500.