Multi-agent traffic simulations to estimate the impact of automated technologies on safety

Abstract

Objective: The objective of this article was to develop a multi-agent traffic simulation methodology to estimate the potential road safety improvements of automated vehicle technologies. Methods: We developed a computer program that merges road infrastructure data with a large number of vehicles, drivers, and pedestrians. Human errors are induced by modeling inattention, aimless driving, insufficient safety confirmation, misjudgment, and inadequate operation. The program was applied to simulate traffic in a prescribed area in Tsukuba city. First, a 100% manual driving scenario was set to simulate traffic for a total preset vehicle travel distance. The crashes from this simulation were compared with real-world crash data from the prescribed area from 2012 to 2017. Thereafter, 4 additional scenarios of increasing levels of automation penetration (including combinations of automated emergency braking [AEB], lane departure warning [LDW], and SAE Level 4 functions) were implemented to estimate their impact on safety.Results: Under manual driving, the system simulated a total of 859 crashes including single-car lane departure, car-to-car, and car-to-pedestrian crashes. These crashes tended to occur in locations similar to real-world crashes. The number of crashes predicted decreased to 156 cases with increasing level of automation. All of the technologies considered contributed to the decrease in crashes. Crash reductions attributable to AEB and LDW in the simulations were comparable to those reported in recent field studies. For the highest levels of automation, no assessment data were available and hence the results should be carefully treated. Further, in modeling automated functions, potentially negative aspects such as sensing failure or human overreliance were not incorporated. Conclusions:We developed a multi-agent traffic simulation methodology to estimate the effect of different automated vehicle technologies on safety. The crash locations resulting from simulations of manual driving within a limited area in Japan were preliminary assessed by comparison with real-world crash data collected in the same area. Increasing penetration levels of AEB and LDW led to a large reduction in both the frequency and severity of rear-end crashes, followed by carto-car head-on crashes and single-vehicle lane departure crashes. Preliminary estimations of the potential safety improvements that may be achieved with highly automated driving technologies were also obtained. ARTICLE HISTORY

The Needed Features of Connected and Automated Vehicles to Prevent Passenger Car Crashes Caused by Driving Errors

“…Recent studies have typically simulated and modelled CAVs' potential operation to evaluate the potential safety impacts. Several studies have focused on some specific traffic environment or intersections, such as roundabouts [24,25], other intersection types [14,25], motorways and freeways [26,27], or other urban and rural road areas [28][29][30]. Several of these studies evaluated the safety impacts with different CAV market penetration rates, which enabled studying the potential safety impacts of mixed traffic consisting of both CAVs and driver-managed vehicles.…”

“…However, the results of the simulation studies depended highly on the defined CAV parameters [29]. Currently, CAVs' operating procedure is not known precisely enough, as data are not available on advanced CAVs' actual operation [28].…”

Survey on Scenario-Based Safety Assessment of Automated Vehicles

“…Therefore, Kitajima et al [31] develop a multi-agent simulation to estimate the impact of AVs. Hundreds of road users (vehicles and pedestrians) are simulated several times in a 6 km × 3 km road layout in a Japanese city, over a total of 80 000 km.…”

Deployment Scenarios for First/Last-Mile Operations With Driverless Shuttles Based on Literature Review and Stakeholder Survey

“…Automated Vehicles (AVs) are one of the largest innovations in transportation research [1], with private automated cars envisioned as the future of passenger transport, promised to reduce traffic congestion [2] and increase traffic safety [3]. In the past decade, several studies have investigated the opportunities and implications of AVs for the private sector, focusing on travel behaviour research, network design, cost/benefit analyses and infrastructure development (examples can be found in [4], [5], [6], [7]).…”