Spatial risk modelling of behavioural hotspots: Risk-aware path planning for autonomous vehicles. (April 2020)

• Record Type: Journal Article
• Authors: Ryan, Cian; Murphy, Finbarr; Mullins, Martin
• Is Part Of: Transportation research. Volume 134(2020)
• Page Start: 152
• Page End: 163
• Publication Date: 2020-04
• Subjects: Autonomous vehicles -- Behavioural hotspots -- Kernel density estimation -- Path planning; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2020.01.024
• Languages: English
• ISSNs: 0965-8564

Abstract

Abstract: Autonomous vehicles (AVs) are expected to considerably improve road safety. That said, accident risk will continue to inflict societal costs. The ability to manage and measure these risks is fundamental to ensure societal acceptance and public adoption of AVs. In particular, the ability to quantitatively compare the safety of AVs relative to human drivers is crucial. Managing risk exposures through driving operational design domains (ODD) will also become prevalent. Ultimately, the deployment of AVs will hinge on the premise that they are safer than humans. In this paper, we posit a methodology to quantitatively evaluate AV risks and minimise their risk exposure once they are publically available. Two contributions are offered. First, we provide a proactive means of evaluating AV risks based on driving behaviour and safety-critical events. This offers statistically meaningful comparisons between humans and AVs given the limitation of current historical data. Second, we propose a novel risk-aware path planning methodology for AVs based on telematics behavioural data. Driving data from a cohort of young human drivers over roughly 270, 000 km in Ireland is used to demonstrate the posited methodology. An unsupervised geostatistical tool called Kernel Density Estimation (KDE) is used to identify "behavioural hotspots" and the risk exposure at each edge or road segment is modelled. The results are incorporated into a path planning algorithm to find safe route paths for AVs, minimising risk exposures. In addition, Self-Organising Maps (SOM) are employed to identify similar risk groups and individual spatial risk patterns are considered.