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Predicting driver rear-end risk-avoidance maneuvers in cut-in scenarios, especially dangerous precrash scenarios, benefits the customization of automatic driving, particularly automatic steering. This paper studies driver rear-end risk-avoidance behaviors in cut-in scenarios on a straight three-lane highway. Data from 24 participants in 1326 valid trials were collected using a motion-based driving simulator. An Eysenck Personality Questionnaire (revised for Chinese participants) was used to obtain the personality traits of the participants. Based on a statistical analysis, the candidate features used in the driver maneuver prediction were determined as a combination of objective risk indicators and driver characteristics. A decision tree-based model was constructed for maneuver prediction in cut-in scenarios. The prediction accuracy of the extracted classification rules was 79.2% for the training data set and 80.3% for the test data set. The most powerful predictive variables were extracted, and their effects on maneuver decisions were analyzed. The results show that driver characteristics strongly influence the prediction of maneuver decisions.

Road network control is challenging but critical in enhancing urban traffic. This paper proposes a management method for a road network with non-signalized intersections in the connected-vehicle environment, which coordinates connected and automated vehicles' movements to make the non-signalized intersections conflict-free and efficient. Firstly, the proposed method projects vehicles in road networks into virtual platoons and builds the road-network-wide conflict-free geometric topology considering the vehicles' conflicting relationships, which describes the geometry car-following relationship in virtual platoons. Then, a distributed linear controller is designed considering vehicle dynamics and communication topology to organize vehicles' movements with the desired geometric topology. Finally, simulations are conducted to verify the proposed method with different traffic demands. Simulation results show the proposed method can significantly improve traffic efficiency, as well as traffic safety. INDEX TERMS Connected and automated vehicles, cooperative traffic control, road network, nonsignalized intersection, distributed control.

Lane change has attracted more and more attention in recent years for its negative impact on traffic safety and efficiency. However, few researches addressed the multi-vehicle cooperation during lane change process. In this article, feasibility criteria of lane change are designed, which considers the acceptable acceleration/deceleration of neighboring vehicles; meanwhile, a cooperative lane change strategy based on model predictive control is proposed in order to attenuate the adverse impacts of lane change on traffic flow. The proposed strategy implements the centralized decision making and active cooperation among the subject vehicle performing lane change in the subject lane and the preceding vehicle and the following vehicle in the target lane during lane change. Using model predictive control, safety, comfort, and traffic efficiency are integrated as the objectives, and lane change process is optimized. Numerical simulation results of the cooperative lane change strategy suggest that the deceleration of following vehicle can be weakened and further the shock wave propagated in traffic flow can be alleviated to some degree compared with traditional lane change.

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