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Full-Text Articles in Controls and Control Theory
Deep Reinforcement Learning And Game Theoretic Monte Carlo Decision Process For Safe And Efficient Lane Change Maneuver And Speed Management, Shahab Karimi
All Dissertations
Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver …
Development Of Autonomous Vehicle Motion Planning And Control Algorithm With D* Planner And Model Predictive Control In A Dynamic Environment, Somnath Mondal
Development Of Autonomous Vehicle Motion Planning And Control Algorithm With D* Planner And Model Predictive Control In A Dynamic Environment, Somnath Mondal
Dissertations, Master's Theses and Master's Reports
The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different …