How do the recognizability and driving styles of automated vehicles affect human drivers' gap acceptance at T- Intersections?. (October 2022)

• Record Type: Journal Article
• Authors: Reddy, Nagarjun; Hoogendoorn, Serge P.; Farah, Haneen
• Is Part Of: Transportation research. Volume 90(2022)
• Page Start: 451
• Page End: 465
• Publication Date: 2022-10
• Subjects: Automated Vehicles -- Mixed Traffic -- Behavioral Adaptation -- Gap Acceptance -- Driving Simulator -- Critical Gap; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2022.09.018
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Human drivers' gap acceptance behavior was studied in a driving simulator. Traffic included human driven vehicles and automated vehicles (AVs) Recognizable and aggressive AVs resulted in larger accepted and critical gaps. Non-recognizable and aggressive AVs resulted in smaller critical gaps. Results suggest that AVs' appearance and driving styles affect human driving behavior. Abstract: Future traffic will be composed of both human-driven vehicles (HDVs) and automated vehicles (AVs). To accurately predict the performance of mixed traffic, an important aspect is describing HDV behavior when interacting with AVs. A few exploratory studies show that HDVs change their behavior when interacting with AVs, being influenced by factors such as recognizability and driving style of AVs. Unsignalized priority intersections can significantly affect traffic flow efficiency and safety of the road network. To understand HDV behavior in mixed traffic at unsignalized priority T -intersections, a driving simulator experiment was set up in which 95 drivers took part in it. The route in the driving simulator included three T -intersections where the drivers had to give priority to traffic on the major road. The participants drove different scenarios which varied in whether the AVs were recognizable or not, and in their driving style (Aggressive or Defensive). The results showed that in mixed traffic having recognizable aggressive AVs, drivers accepted significantly larger gaps (and had larger critical gaps) when merging in front of AVs as compared to mixed traffic having either recognizable defensive AVs or recognizable mixed AVs (composed of both aggressive and defensive). This was not the case when merging in front of an HDV in the same scenarios. Drivers had significantly smaller critical gaps when driving in traffic having non-recognizable aggressive AVs compared to non-recognizable defensive AVs. The findings suggest that human drivers change their gap acceptance behavior in mixed traffic depending on the combined effect of recognizability and driving style of AVs, including accepting shorter gaps in front of non-recognizable aggressive AVs and changing their original driving behavior. This could have implications for traffic efficiency and safety at such priority intersections. Decision makers must carefully consider such behavioral adaptations before implementing any policy changes related to AVs and the infrastructure.