Conditional Artificial Potential FieldBased Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario Carfollowing is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the carfollowing control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehiclesxe2x80x99 abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learningbased lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an intervalbased support vector machine is designed to predict the vehiclesxe2x80x99 lane change intention utilizing limited lowlevel vehicle status through vehicletovehicle communication. Then, a conditional artificial potential field method is used to design the carfollowing controller by incorporating the lanechange intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehiclexe2x80x99s lane change intention more accurately. The autonomous vehicle avoids collisions with a lanechanging connected unintelligent vehicle with reliable safety and favorable dynamic performance.

Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm Lane change is a challenging task which requires delicate actions to ensure safety and comfort. Some recent studies have attempted to solve the lanechange control problem with Reinforcement Learning (RL), yet the action is confined to discrete action space. To overcome this limitation, we formulate the lane change behavior with continuous action in a modelfree dynamic driving environment based on Deep Deterministic Policy Gradient (DDPG). The reward function, which is critical for learning the optimal policy, is defined by control values, position deviation status, and maneuvering time to provide the RL agent informative signals. The RL agent is trained from scratch without resorting to any prior knowledge of the environment and vehicle dynamics since they are not easy to obtain. Seven models under different hyperparameter settings are compared. A video showing the learning progress of the driving behavior is available. It demonstrates the RL vehicle agent initially runs out of road boundary frequently, but eventually has managed to smoothly and stably change to the target lane with a success rate of 100% under diverse driving situations in simulation.