Analysis of Implicit Communication of Motorists and Cyclists in Intersection Using Video and Trajectory Data.

The interaction of automated vehicles with vulnerable road users is one of the greatest challenges in the development of automated driving functions (ADF). In order to improve efficiency and ensure the safety of mixed traffic, ADF need to understand the intention of vulnerable road users, to adapt to their driving behavior, and to show its intention. However, this communication may occur in an implicit way, meaning they may communicate with vulnerable road users by using dynamic information, such as speed, distance, etc. Therefore, investigating patterns of implicit communication of human drivers with vulnerable road users is relevant for developing ADF. The aim of this study is to identify the patterns of implicit communication of human drivers with vulnerable road users. For this purpose, the interaction between right-turning motorists and crossing cyclists was investigated at a traffic light controlled urban intersection. In the scenario, motorists and cyclists had a green signal at the same time, but cyclist had right-of-way. Using the Application Platform for Intelligent Mobility (AIM) Research Intersection, trajectory and video data were recorded at an intersection in Braunschweig, Germany. Data had been recorded for 4 weeks. Based on the criticality metric post-encroachment time (PET) and quality of the recorded trajectory, 206 cases of interaction were selected for further analyses. According to the video annotation, when approaching the intersection, three common communication patterns were identified: (1) no yield, motorists, who should yield to cyclists, crossed the intersection first while forcing right-of-way; (2) active yield, motorists, who were in front of cyclists, gave the right-of-way; (3) passive yield, motorists, who were behind cyclists, had to give the right-of-way. The analysis of the trajectory data revealed different patterns of changes in time advantage in these three categories. Additionally, the communication patterns were evaluated with regard to frequency of occurrence, efficiency, and safety. The findings of this study may provide knowledge for the implementation of a communication strategy for ADF, contributing to traffic efficiency as well as ensuring safety in the interaction with vulnerable road users.

Online risk estimation of critical and non-critical interactions between right-turning motorists and crossing cyclists by a decision tree.

One of the most critical situations in urban areas is when motorists turn right in an intersection and cyclists cross the road. Many of those crashes result in severe consequences for cyclists. In order to increase the safety of cyclists, especially in the case of conflicts with right-turning vehicles, an online infrastructure-based assistance system may be a promising solution warning drivers and cyclists when a conflict or crash is predicted. By means of automated video traffic detection, the resulting trajectories of road users can be analysed and a warning can be sent to vehicles and cyclists equipped with vehicle-to-anything communication (V2X) when a high risk is estimated. An approach for online risk estimation was developed combining the surrogate measure of safety (SMoS) gap time (GT) with trajectory prediction-based estimates of the time-to-arrival (TTA) or distance to conflict point (DCp) and velocity (v). A decision tree as classifier of risk levels based on the previous named risk features was trained to model the risks perceived by humans. Expert ratings of traffic conflict scenes were used to build a model, apply the model, and improve it in the field. The warning system was evaluated by test drives in real traffic at the urban AIM Research Intersection in Braunschweig, Germany. In general, the system warned reliably. In approximately 67% of the trials, it was assessed as helpful.

Behavioral adaptation of young and older drivers to an intersection crossing advisory system.

An advanced driver assistance system (ADAS) provided information about the right of way regulation and safety to cross an upcoming intersection. Effects were studied in a longer-term study involving 18 healthy older drivers between the ages of 65 and 82 years and 18 healthy young drivers between the ages of 20 and 25 years. Participants repeatedly drove 25 km city routes in eight sessions on separate days over a period of two months in a driving simulator. In each age group, participants were randomly assigned to the control (no ADAS) and treatment (ADAS) group. The control group completed the whole experiment without the ADAS. The treatment group drove two sessions without (sessions 1 and 7) and six times with ADAS. Results indicate effects of ADAS on driving safety for young and older drivers, as intersection time and percentage of stops decreased, speed and critical intersection crossings increased, the number of crashes was lower for treatment groups than for control groups. The implications of results are discussed in terms of behavioral adaptation and safety.

Longer-term effects of ADAS use on speed and headway control in drivers diagnosed with Parkinson's disease.

OBJECTIVE: An advanced driver assistance system (ADAS) provided information about speed limits, speed, speeding, and following distance. Information was presented to the participants by means of a head-up display. METHODS: Effects of the information on speed and headway control were studied in a longer-term driving simulator study including 12 repeated measures spread out over 4 weeks. Nine healthy older drivers between the ages of 65 and 82 years and 9 drivers between the ages of 68 and 82 years diagnosed with Parkinson's disease (PD) participated in the study. Within the 4 weeks, groups completed 12 consecutive sessions (10 with ADAS and 2 without ADAS) in a driving simulator. RESULTS: Results indicate an effect of ADAS use on performance. Removing ADAS after short-term exposure led to deterioration of performance in all speed measures in the group of drivers diagnosed with PD. CONCLUSIONS: These results suggest that provision of traffic information was utilized by drivers diagnosed with PD in order to control their speed.

Intersection assistance: a safe solution for older drivers?

Within the next few decades, the number of older drivers operating a vehicle will increase rapidly (Eurostat, 2011). As age increases so does physical vulnerability, age-related impairments, and the risk of being involved in a fatal crashes. Older drivers experience problems in driving situations that require divided attention and decision making under time pressure as reflected by their overrepresentation in at-fault crashes on intersections. Advanced Driver Assistance Systems (ADAS) especially designed to support older drivers crossing intersections might counteract these difficulties. In a longer-term driving simulator study, the effects of an intersection assistant on driving were evaluated. 18 older drivers (M=71.44 years) returned repeatedly completing a ride either with or without a support system in a driving simulator. In order to test the intersection assistance, eight intersections were depicted for further analyses. Results show that ADAS affects driving. Equipped with ADAS, drivers allocated more attention to the road center rather than the left and right, crossed intersections in shorter time, engaged in higher speeds, and crossed more often with a critical time-to-collision (TTC) value. The implications of results are discussed in terms of behavioral adaptation and safety.