Smart Prediction-Planning Algorithm for Connected and Autonomous Vehicle Based on Social Value Orientation

Abstract

To improve the adaptability of Connected and Automated Vehicles (CAVs) in mixed traffic, this study proposes a prediction model training indicator that comprehensively considers drivers' Social Value Orientation (SVO) and planning goals. Active Influence Factor (AIF) is used as the goal to predict the future safety loss and consistency loss of CAVs. Second, an objective function based on SVO is constructed to understand the driver's characteristics to evaluate the safety, comfort, efficiency, and consistency of candidate trajectories. The results showed that integrating SVO and consistency functions can help ensure that CAVs drive under a more stable risk potential energy field. The prediction planning model that considers SVO can improve the reliability of the CAV output trajectory to a certain extent. The prediction planning under the AIF has better accuracy and stability of the output trajectory; however, it still has strong adaptability and superiority under different sensitivity parameters. The minimum and maximum standard deviations of our model are 0.78 and 0.78 m, respectively, whereas the minimum and maximum standard deviations of the comparative model reach 2.07 and 4.56 m, respectively. The minimum standard deviation of the other comparative model reaches 1.35 m, and the maximum standard deviation reaches 4.45 m.