Multi-driver agent-based traffic simulation systems for evaluating the effects of advanced driver assistance systems on road traffic accidents

Yuhara, Naohiro; Tajima, Jun

doi:10.1007/s10111-006-0045-9

Cited by 16 publications

(12 citation statements)

References 6 publications

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“…The 5 most relevant human error types reported in crashes occurring in Japan in 2015 (Japan Police 2015) were selected as targets for driver error modeling. These error types included inattention, aimless driving, insufficient safety confirmation, misjudgment, and inadequate operation (Yuhara and Tajima 2006) and provided a coverage of more than 80% of the errors that cause crashes in each of the 5 crash types targeted by the current simulation study ( Figure A9, see online supplement). The techniques applied to model driver behavior (including perception, decision making, and longitudinal and lateral vehicle control), driver diversity, and human errors are described in detail in the Appendix (see online supplement).…”

Section: Driver Behavior Diversity and Error Simulation Modelsmentioning

confidence: 99%

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

Kitajima

Shimono

Tajima³

et al. 2019

Traffic Injury Prevention

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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

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Section: Driver Behavior Diversity and Error Simulation Modelsmentioning

confidence: 99%

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

Kitajima

Shimono

Tajima³

et al. 2019

Traffic Injury Prevention

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“…A majority of the models (Furukawa et al, 2009;Kitaoka et al, 2009;Yuhara & Tajima, 2006) include explicit, probabilistic mechanisms for error generation, at all three stages of processing outlined above. The errors are introduced in order to have simulations generate accidents, with the purpose of estimating safety impacts of various active safety systems.…”

Section: Simulation Platformsmentioning

confidence: 99%

“…The errors are introduced in order to have simulations generate accidents, with the purpose of estimating safety impacts of various active safety systems. Kitaoka et al (2009) and Yuhara and Tajima (2006) also included basic on/off road visual distraction behavior. Wood et al (2003) did not include any explicit accident causation mechanisms, but instead manually tuned decisionmaking of their driver models to reproduce a specific head-on collision event from real traffic.…”

Section: Simulation Platformsmentioning

confidence: 99%

“…Furthermore, Kitaoka et al (2009) put a stronger emphasis than the others on driver characteristics and states, and Yuhara and Tajima (2006) were alone in including a specific evasive maneuvering mode, triggered by a TTC criterion. However, thorough validation on accident-related data from real traffic seems to be lacking for all models.…”

Section: Simulation Platformsmentioning

confidence: 99%

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A Review of Near-Collision Driver Behavior Models

Markkula

Benderius²,

Wolff³

et al. 2012

Hum Factors

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“…The entire simulation environment is developed as a multi-agent based model of how Nav2Nav would operate. The model was built similar to the work done in [11]. Several entities are simulated in this phase including cars, drivers, and Nav2Nav units.…”

Section: Nav2nav Simulationmentioning

confidence: 99%

Collaborative navigation systems for collision avoidance

Karimi

Zimmerman

Nawn

et al. 2010

Proceedings of the 6th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing

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Accidents on roads are one of the main causes of human loss and damages to property every day and anywhere that vehicles travel. Communications-based approaches to reducing highway accidents include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, while V2V and V2I are promising approaches for reducing accidents, they require installation of new sensors in vehicles and the information they can produce is limited. In this paper, we present the navigation-to-navigation (Nav2Nav) approach for reducing accidents on roads which is different from V2V and V2I from several perspectives including: (a) the widespread availability and use of navigation systems with no installation requirement, (b) the familiarity and acceptance of navigation systems by many people around the word, (c) the availability of map databases in navigation systems which facilitates map matching (the process of finding the correct segment of road as opposed to raw positioning data) and prediction in navigation, and (d) the availability of routes requested by drivers.

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Multi-driver agent-based traffic simulation systems for evaluating the effects of advanced driver assistance systems on road traffic accidents

Cited by 16 publications

References 6 publications

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

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

A Review of Near-Collision Driver Behavior Models

Collaborative navigation systems for collision avoidance

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