期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 10, 页码 16983-17002出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3160932
关键词
Autonomous vehicles; Cameras; Roads; Meteorology; Vehicle dynamics; Navigation; Standards; Cooperative autonomous driving; motion prediction; perception; state estimation; vulnerable road users
资金
- Department of Mechanical Engineering, Virginia Tech
- Safe-D Project (Safety through Disruption and Safe-D National UTC) [69A3551747115]
This study provides a comprehensive survey of motion prediction and state estimation literature, which is essential for path planning and navigation functionalities of autonomous vehicles. The study summarizes significant progress made in complex traffic environments and highlights the research challenges that need to be overcome.
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.
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