State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey

69A3551747115This manuscript is published in IEEE Transactions on Intelligent Transportation Systems. Please cite:P. Ghorai, A. Eskandarian, Y. -K. Kim and G. Mehr, "State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 16983-17002, Oct. 2022, doi: 10.1109/TITS.2022.3160932.The digital object identifier for this article is https://doi.org/10.1109/TITS.2022.3160932The recent progress in autonomous vehicle research and development has led to increasingly widespread testing of fully autonomous vehicles on public roads, where complex traffic scenarios arise. Along with these vehicles, partially autonomous vehicles, manually-driven vehicles, pedestrians, cyclists, and some animals can be present on the road, to which autonomous vehicles must react. This study focuses on a comprehensive survey of the literature on motion prediction and state estimation of vehicles and VRUs, which are essential for path planning and navigation functionalities of an autonomous vehicle. Motion prediction and state estimation methods utilize the vehicle's own sensory perception capabilities and information obtained through cooperative perception from V2V and V2X connections. This survey summarizes the significant progress that has been made in both categories, discusses the most promising results to date and outlines critical research challenges that need to be overcome to achieve full autonomy, from an ego vehicle's perspective in mixed traffic environments