Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance

Abstract

Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles (AV) are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about the driving environment. Studies have proved that automated driving have the potential to decrease traffic congestion by reducing the time headway (THW), enhancing the traffic capacity and improving the safety margins in car following. Despite different encouraging factors, automated driving raise some concerns such as possible loss of situation awareness, overreliance on automation and system failure. This paper aims to investigate the effects of AV on driver's behavior and traffic performance. A literature review was conducted to examine the AV effects on driver's behavior. Findings from the literature survey reveal that conventional vehicles (CV), i.e. human driven, which are driving close to a platoon of AV with short THW, tend to reduce their THW and spend more time under their critical THW. Additionally, driving highly AV reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of AV on traffic performance, a simulation case study consisting of a 100% AV scenario and a 100% CV scenario was performed using microscopic traffic simulation. Outputs of this simulation study reveal that the positive effects of AV on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably improved by 8.09% during p.m. peak hours in the AV scenario, while the average travel speed enhanced relatively by 8.48%. As a consequent, the average travel time improved by 9.00% in the AV scenario. The outcome of this study jointly with the previous driving simulator studies illustrates a successful practice of microscopic traffic simulation to investigate the effects of AV. However, further development of the microscopic traffic simulation models are required and further investigations of mixed traffic situation with AV and CV need to be conducted.

Influence of Autonomous Vehicles on Freeway Traffic Performance for Undersaturated Traffic Conditions

“…This can reduce or optimize gaps between vehicles on the road and results in a smooth and safe traffic flow. It was found that a small percentage of vehicles equipped with these technologies results in a drastic reduction in traffic congestion, and thus increases traffic stability and road capacity (Kesting et al 2007, Ioannou and Chien 1993, Aria et al 2016, Tientrakool et al 2011, Arnaout and Bowling 2011. This is consistent with the findings of the report Co-published by World Economic Forum and Boston consulting group in 2018.…”

“…In total 18 scenarios were simulated. Each scenario was simulated for 5 runs using different random seed numbers, then the trimmed average of the three middle values for each scenario were considered as the average result (Aria et al 2016, Ngan et al 2004.…”

Introduction of Autonomous Vehicles: Roundabouts Design and Safety Performance Evaluation

“…Another very important setting parameter-in order to enhance the simulation-was the insertion of Desired Speed Decision at 30 km/h, placed 15 m before the roundabout along each arm. The Autonomous Vehicles (AVs) calibration was done by using and adjusting VISSIM parameter set for the "Driving Behaviour Parameter Set\Wiedemann 99\Following" according to the assumptions made by [9,16,17].…”

“…Furthermore, the optimal setting obtained by 10 calibration parameters for AVs, all labeled with a "CC" prefix-each of the parameters controls a unique aspect of the car following model as illustrated in Figure 6 was [17,18] For the above reported roundabout schemes, Omisalj and Malinska, four different traffic configurations (TC) were considered:…”

“…Specific parameters (VISSIM) have been identified as levers-allowing the modification of vehicle behaviour to represent a possible future for AVs. Behavioural change in VISSIM refers to a specific vehicle type [12,13]. This research will also show how the mechanisms by which AVs could impact traffic flow, network performance and road capacity are, in the main, reasonably well understood and broadly accepted.…”