Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI To Detect Near-Miss Collisions

Pourhomayoun, Mohammad; San Jose State University. College of Business. Mineta Transportation Institute; California State University, Los Angeles

doi:10.31979/mti.2024.2350

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Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI To Detect Near-Miss Collisions

2024-10-01
By Pourhomayoun, Mohammad

[PDF-4.29 MB]

English

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Details:

Creators:

Pourhomayoun, Mohammad
Corporate Creators:

San Jose State University. College of Business. Mineta Transportation Institute ; California State University, Los Angeles

San Jose State University. College of Business. Mineta Transportation Institute ; California State University, Los Angeles Less -
Corporate Contributors:

State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Cyclists Machine Learning Near Crashes Pedestrian Safety Traffic Safety
Publication/ Report Number:

24-34
DOI:

https://doi.org/10.31979/mti.2024.2350
Resource Type:

Tech Report
Geographical Coverage:

California ; United States

California ; United States Less -
Corporate Publisher:

San Jose State University. College of Business. Mineta Transportation Institute
Abstract:

Near-Miss Collisions are events that, with a slight change in position or timing, could have resulted in a collision, which could have caused severe injury or property damage. Understanding near-miss collisions can help identify risks and potentially improve road safety. In this project, we developed an effective end-to-end system based on advanced artificial intelligence (AI) models and computer vision algorithms to detect and report near-miss collisions as an important indicator to identify and measure safety risks, especially in specific circumstances such as a right turn on a red light. The main objective is to improve the safety of pedestrians and bicyclists, by applying automated AI-powered systems to detect accident risks for pedestrians and cyclists. The developed system includes algorithms for detecting and tracking all traffic objects including pedestrians and bicyclists, as well as algorithms for estimating collision risks and detecting near misses. We evaluated the developed system on real videos captured by actual traffic cameras in the city of Los Angeles. Despite the low quality of some of the videos, our results demonstrate high accuracy of the developed models in identifying traffic collision risks and detecting near-misses. The information generated by the developed system allows us to enhance safety measures for pedestrians and bicyclists while simultaneously optimizing traffic flow.
Format:

PDF
Funding:

ZSB12017-SJAUX
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:b85f18e98dd3d24c86d87013ae5801ece5e94c42b6e3ad7d761bfa1949bb5b44f74edb1d55b17c2820ba2442a42eccf2de3803fb15e74db38eb66baefc1f9b1c
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https://rosap.ntl.bts.gov/view/dot/78240/dot_78240_DS1.pdf
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Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI To Detect Near-Miss Collisions

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