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

Ghorai, Prasenjit; Eskandarian, Azim; Kim, Young-Keun; Meh, Goodarz; Virginia Tech University. Mechanical Engineering Department; Handong Global University. School of Mechanical and Control Engineering

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

2022-04-13
By Ghorai, Prasenjit ; Eskandarian, Azim ; Kim, Young-Keun ; ...

[PDF-4.44 MB]

English

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Details:

Creators:

Ghorai, Prasenjit ; Eskandarian, Azim ; Kim, Young-Keun ; Meh, Goodarz

Ghorai, Prasenjit ; Eskandarian, Azim ; Kim, Young-Keun ; Meh, Goodarz Less -
Corporate Creators:

Virginia Tech University. Mechanical Engineering Department ; Handong Global University. School of Mechanical and Control Engineering

Virginia Tech University. Mechanical Engineering Department ; Handong Global University. School of Mechanical and Control Engineering Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; Safety through Disruption (Safe-D) University Transportation Center (UTC)

United States. Department of Transportation. University Transportation Centers (UTC) Program ; Safety through Disruption (Safe-D) University Transportation Center (UTC) Less -
Subject/TRT Terms:

[+]

Autonomous Vehicles Cameras Meteorology Navigation Roads Standards Vehicle Dynamics
Resource Type:

Manuscript
Geographical Coverage:

United States
Corporate Publisher:

United States. Department of Transportation. National Transportation Library [distributor]
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.
Content Notes:

This 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.2022.3160932
Format:

PDF
Funding:

69A3551747115
Collection(s):

University Transportation Centers
Main Document Checksum:

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urn:sha256:b5ddb31bdc90bfb3ef63b2849a6e45667116f2d866b29387568c962221a3a262
Download URL:

https://rosap.ntl.bts.gov/view/dot/62533/dot_62533_DS1.pdf
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State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey

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