Can Automated Vehicles Improve Cyclist Safety in Urban Areas?

How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists' yielding behavior using naturalistic data

When a cyclist's path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists' (other road users') behavior will be increasingly important to ensure safety. To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists' behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions. In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists' crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists' behavioral cues obtained from sensors. Both kinematics and cyclists' behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist's yielding behavior. This research shows that adding information about the cyclists' behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety.

Application of surrogate safety measures in higher levels of automated vehicles simulation studies: A review of the state of the practice

OBJECTIVE

Surrogate safety measures (SSMs) are developed and applied as alternatives or complements of safety analyses mainly due to important road crash data availability and reliability limitations. Automated vehicles (AVs) have recently emerged as a prominent solution to mitigate transport externalities and increase road traffic safety. Due to the novelty of the technology and the lack of real-world data, traffic simulation combined with SSMs is the most common approach to quantify their impact. This study aims to provide an overview of the state of the practice and, more specifically, examine the applicability of applied SSMs on higher levels of AVs (HAVs).

METHODS

The methodological approach consists of a comprehensive literature search, which aims to provide an overview of the applied SSMs, followed by a critical assessment of the findings.

RESULTS

In total, 17 studies and 11 different SSMs were identified and reviewed. Findings suggest that available SSMs are suitable measures to appropriately estimate the relative safety performance of HAVs and indicate their potential implications due to their expected rule-based driving behavior. However, in some cases, it was noticed that they could not efficiently capture the technological capabilities of HAVs, e.g., shorter headways and faster reaction times, which may lead to false alarms.

CONCLUSIONS

Despite the available evidence, there are still significant gaps and certain limitations, as no comparisons between different measures exist, or the validity of the applied measures could not be assessed based on historical road crash data. This work aims to help researchers and practitioners choose the most appropriate SSMs to evaluate HAVs' safety performance. Finally, several research gaps are identified, and recommendations for potential future research directions are presented.

Differences in Pedestrian Behavior at Crosswalk between Communicating with Conventional Vehicle and Automated Vehicle in Real Traffic Environment

In this study, we examine the differences in pedestrian behavior at crosswalks between communicating with conventional vehicles (CVs) and automated vehicles (AVs). To analyze pedestrian behavior statistically, we record the pedestrian’s position (x- and y-coordinates) every 0.5 s and perform a hot spot analysis. A Toyota Prius (ZVW30) is used as the CV and AV, and the vehicle behavior is controlled using the Wizard of Oz method. An experiment is conducted on a public road in Odaiba, Tokyo, Japan, where 38 participants are recruited for each experiment involving a CV and an AV. The participants cross the road after communicating with the CV or AV. The results show that the pedestrians can cross earlier when communicating with the CV as compared with the AV. The hot spot analysis shows that pedestrians who communicate with the CV decide to cross the road before the CV stops; however, pedestrians who communicate with the AVs decide to cross the road after the AV stops. It is discovered that perceived safety does not significantly affect pedestrian behavior; therefore, earlier perceived safety by drivers’ communication and external human–machine interface is more important than higher perceived safety for achieving efficient communication.

Cyclists’ Crossing Intentions When Interacting with Automated Vehicles: A Virtual Reality Study

Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions between cyclists and AVs exists. This study aims to determine the main factors influencing cyclists’ crossing intentions when interacting with an automated vehicle as compared to a conventional vehicle (CV) using a 360° video-based virtual reality (VR) method. The considered factors in this study included vehicle type, gap size between cyclist and vehicle, vehicle speed, and right of way. Each factor had two levels. In addition, cyclist’s self-reported behavior and trust in automated vehicles were also measured. Forty-seven participants experienced 16 different crossing scenarios in a repeated measures study using VR. These scenarios are the result of combinations of the studied factors at different levels. In total, the experiment lasted 60 min. The results show that the gap size and the right of way were the primary factors affecting the crossing intentions of the individuals. The vehicle type and vehicle speed did not have a significant effect on the crossing intentions. Finally, the 360° video-based VR method scored relatively high as a research method and comparable with the results of a previous study investigating pedestrians’ crossing intentions confirming its suitability as a research methodology to study cyclists’ crossing intentions.