More Like this

1. Understanding Reliance Decisions in Automated Vehicles Using Random Forest Analysis

   https://doi.org/10.1177/10711813241262982  

   Ma, Xingjian; Xiao, Xizi; Mehta, Ranjana; McDonald, Anthony_D (August 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Driver reliance on automated vehicles (AV) is a critical component of safety particularly during high-risk traffic scenarios. Factors that influence reliance, including trust, situation awareness, fatigue, and demographics, have been independently explored; however, few analyses have investigated predicting AV reliance and compared factors comprehensively. The goals of this study were to develop a random forest (RF) model to predict reliance and to analyze the importance of factors for reliance decisions. We leveraged data from a driving simulation study where participants encountered four traffic events including responding to an illegal vehicle crossing, managing construction zones, stopping at a vandalized stop sign, and a pedestrian detection task. The dataset included reliance decisions and subjective assessments of dispositional trust, situational trust, fatigue, and workload. An RF model fit to the dataset using cross validation achieved an average AUC of 0.81 and accuracy of 0.77 and situational trust emerged as the most influential predictor. 

   more » « less
2. Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

   https://doi.org/10.1177/00187208221088358  

   Ma, Zheng; Zhang, Yiqi (April 2022, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience. 

   more » « less
3. Real-Time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy

   https://doi.org/10.1177/0361198119843255  

   Rahman, Mizanur; Islam, Mhafuzul; Calhoun, Jon; Chowdhury, Mashrur (May 2019, Transportation Research Record: Journal of the Transportation Research Board)

   Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency communication-enabled device with an activated pedestrian safety application in their hand-held device all the time. Because of this, multiple traffic cameras at a signalized intersection could be used to accurately detect and locate pedestrians using deep learning, and broadcast safety alerts related to pedestrians to warn connected and automated vehicles around signalized intersections. However, the unavailability of high-performance roadside computing infrastructure and the limited network bandwidth between traffic cameras and the computing infrastructure limits the ability of real-time data streaming and processing for pedestrian detection. In this paper, we describe an edge computing-based real-time pedestrian detection strategy that combines a pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining high pedestrian detection accuracy. We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined pedestrian detection accuracy. The performance of the pedestrian detection strategy is measured in relation to pedestrian classification accuracy with varying peak signal-to-noise ratios. The analyses reveal that we detect pedestrians by maintaining a defined detection accuracy with a peak signal-to-noise ratio 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31× reduction. 

   more » « less
4. Safety Assessment of Cooperative Platooning in Mixed Traffic

   https://doi.org/10.3390/engproc2023036038  

   Park, B. Brian; Lee, Hyejin; Yun, Ilsoo; Park, Jeehyung (July 2023, Engineering proceedings)

   The safety impacts of cooperative platooning in mixed traffic consisting of human-driven, con-nected, and connected-automated vehicles were evaluated. The cooperative platooning in mixed traffic control algorithm evaluated is the Cooperative Adaptive Cruise Control with unconnected Vehicle (CACCu) with an unconnected vehicle. Its safety and string stability were evaluated using a high-fidelity simulation based on real-world vehicle trajectories. An Adaptive Cruise Control (ACC) algorithm was selected for comparison purposes. The results indicate that the cooperative platooning in mixed traffic control algorithm (CACCu) maintains string stability and performs more safely than the ACC. 

   more » « less
5. Analysis of Driver Behavior in Mixed Autonomous and Non-autonomous Traffic Flows

   https://doi.org/10.1177/1071181322661305  

   Liu, Jundi; Boyle, Linda Ng (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Autonomous vehicles are expected to improve road safety and efficiency in future transportation systems. A driving simulator study was designed to identify driving styles and the cooperation between human drivers and other AVs. The study captured driver’s following behavior in a fully autonomous driving environment at unsignalized intersections. Participants were asked to make a series of maneuvers (straight through intersection, left turn, and right turn) in two different speed conditions (30, 40 mph) and two different traffic density conditions (with or without other traffic). Analysis of Variance showed that drivers had a significantly larger deviation (defined as the area between two trajectories) during left turn maneuvers when they were traveling at higher speeds. Moreover, the first turning operation had smaller deviation than the second turning operation. The findings have implications for the design of driver-assistance guidance systems in future mixed autonomous and non-autonomous traffic flows. 

   more » « less