Enhancing Pedestrian and Bicyclist Safety through Abnormal Driving Behavior Detection

Abnormal driving poses a significant risk not only to the driver (referred to as the ego vehicle) but also to other road users, particularly pedestrians and bicyclists. Existing literature (Wu et al., 2018) has shown that early detection and intervention in cases of abnormal driving can prevent traffic accidents or at least reduce their severity. To this end, this project is focused on creating an application designed to improve the safety of pedestrians and bicyclists by identifying abnormal driving behaviors. Current research on detecting abnormal driving behaviors largely depends on the use of onboard sensors that monitor various aspects of the driver’s physical state and driving patterns, such as facial and gaze direction, blood pressure, and heart rate. However, this approach requires drivers to equip their vehicles with specific devices, often at their own expense, which may hinder the popularization of such technologies. An alternative and more cost-effective solution involves using roadside sensors, like cameras, LIDAR, and RADAR, to detect abnormal driving. This method analyzes and predicts vehicle trajectories based on live-streamed data from roadside sensors. If a vehicle’s trajectory significantly deviates from its predicted path, it can be identified as abnormal. Despite its promising results, this method has strict requirements regarding the accuracy of trajectory prediction. Otherwise, too many false alarms could erode public trust in the technology. Addressing these challenges and improving the reliability and accuracy of abnormal driving detection methods is a key goal of this project. The project is set to advance traffic safety technologies and methodologies, starting with the development of an advanced trajectory prediction framework. This framework will be designed to accurately simulate and predict vehicle-pedestrian interactions, enabling effective detection of abnormal driving behaviors in various traffic scenarios. The research will focus on refining data collection and analysis methods using YOLO image-processing techniques on live-streamed videos from roadside cameras. This effort will improve the calibration of prediction models and expand the research team's database on interactions among vehicles, pedestrians, and bicyclists. Additionally, the team will develop specialized software that processes this data in real-time, which is critical for implementing these advancements in urban traffic management systems. As the project progresses, the team will explore potential patent filings for the unique methods and technologies developed, including specific applications of YOLO for traffic surveillance. The team plans to prepare research findings for publication and presentation at international conferences, sharing methodologies and their implications for traffic safety and urban planning. Furthermore, educational materials based on anticipated research findings will be developed to train and inform other researchers, students, and practitioners in the field. This initiative will aim to foster further research and innovation in traffic management and safety technologies. This project aims to significantly impact the transportation system by enhancing safety, reliability, and cost-effectiveness. Utilizing advanced trajectory prediction frameworks and sophisticated image processing technologies like YOLO, the project aims to improve road safety for vulnerable users through non-intrusive, infrastructure-based monitoring systems. These technologies are expected to reduce accident rates and can be integrated into existing urban infrastructure, reducing costs and easing implementation across various settings. The outcomes could lead to changes in regulatory, legislative, and policy frameworks by setting new standards for traffic monitoring and encouraging the adoption of proactive safety measures. Potential patents and new products developed from this project may influence urban planning and vehicle compliance standards, promoting the use of smart technology in traffic management systems. Overall, the project not only aims to enhance the safety and efficiency of transportation systems but also serves as a model for future traffic safety management initiatives.

Language

  • English

Project

  • Status: Active
  • Funding: $42123
  • Contract Numbers:

    69A3552348336

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    Department of Transportation
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Managing Organizations:

    Office of the Assistant Secretary for Research and Technology

    Department of Transportation
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Stearns, Amy

  • Performing Organizations:

    University of Wisconsin-Milwaukee

    Department of Urban Planning/Institute for Physical Infrastructure and Transportation
    Milwaukee, WI  United States 
  • Principal Investigators:

    Shi, Tom

  • Start Date: 20240601
  • Expected Completion Date: 20250531
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01923822
  • Record Type: Research project
  • Source Agency: Center for Pedestrian and Bicyclist Safety
  • Contract Numbers: 69A3552348336
  • Files: UTC, RIP
  • Created Date: Jul 8 2024 2:54PM