A Heterogeneous Traffic Virtual-Reality Simulator to Study Irritation/Anger and Driving Behavior under Adverse Conditions

Abstract

To analyse driver's behaviour in heterogeneous chaotic traffic, simulators are important as experimentation in real traffic is dangerous. Simulators using Virtual Reality HMD provide a 360degree view enabling almost realistic experience. In our research, we focus on studying the driver's response when forced to follow a slow-moving vehicle with or without lane regulation. We collected self-reports of emotions-anger/frustration. Set as a first-person view through the windshield, a player controls the virtual car in simulated (game engine Unity3D) traffic with a steering wheel fitted with hand operated acceleration and brake hardware controls. Road scenarios similar to a typical Indian condition are replicated and each individual vehicle type is implemented as Artificial Intelligent (AI) bot. These bots interact with the player forcing her/him to react to the emergent situations. The player's goal is to overtake a very slow-moving vehicle ahead blocking the traffic and the scenarios presented were: a) no-lanes, b) lane change possible only at certain stretches of the road and c) lanes demarcated by solid barricades. Traffic sounds were added for natural effect. Participants with and without real-life driving experience were recruited. The time to reach the finish line was the least in the no-lane condition and the driver was able to manoeuvre through the gaps between vehicles. Designated lane-change stretches required quick responses and speed-prediction skills, in the absence of which collisions were observed and also time to finish was longer. Though lane marking and discipline is promoted for safety, in heterogeneous traffic with vehicles of varying engine capacity, strict lane adherence could lead to massive traffic jams and driver frustration/road rage.

Improving the Capacity and Level of Service at the Sixth Circle in Jordan by Using Traffic Signals and Roundabout Metering Approach

“…For instance, model validation can be utilized for testing the accuracy of the model by comparing the traffic flow data gathered from the field with that generated by the model [34,35]. It is essential for identifying important issues on model validation such as collecting the essential data, quantifying uncertainties, predicting MOEs, and establishing the comprehensive meaning of model validation [36][37][38]. It is related to the model calibration process directly as adjustments in calibration are essential to boost the ability of the model for replicating the traffic dynamics observed from the field [39,40].…”

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