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

TLDR

Autonomous vehicle research has progressed to widespread public‑road testing, where complex traffic scenarios involve fully autonomous, partially autonomous, manually‑driven vehicles, pedestrians, cyclists, and animals. The study surveys motion prediction and state estimation literature for vehicles and VRUs, essential for path planning and navigation. It focuses on methods that use vehicle sensory perception and cooperative V2V/V2X information. The survey summarizes progress, highlights promising results, and outlines critical research challenges for achieving full autonomy in mixed traffic.

Abstract

The 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.