No abstract

No abstract

No abstract

No abstract

• Drivers' perceptions of oncoming trains and decision making regarding their crossing behaviours were examined • Drivers identified the presence of trains 2km away and their movement at 1.6km away, with high variability between participants • Most participants underestimated the speed of oncoming trains, particularly when they were travelling at higher speeds

No abstract

Automatically assessing driving behaviour against traffic rules is a challenging task for improving the safety of Automated Vehicles (AVs). There are no AV specific traffic rules against which AV behaviour can be assessed. Moreover current traffic rules can be imprecisely expressed and are sometimes conflicting making it hard to validate AV driving behaviour. Therefore, in this paper, we propose a Defeasible Deontic Logic (DDL) based driving behaviour assessment methodology for AVs. DDL is used to effectively handle rule exceptions and resolve conflicts in rule norms. A data-driven experiment is conducted to prove the effectiveness of the proposed methodology.

No abstract