Analyzing and modelling drivers’ deceleration behaviour from normal driving
journal contributionposted on 14.07.2017 by Penny Deligianni, Mohammed Quddus, Andrew Morris, Aaron Anvuur, Steven Reed
Any type of content formally published in an academic journal, usually following a peer-review process.
Most research in vehicle automation has mainly focused on the safety aspect with only limited studies on occupants’ discomfort. In order to facilitate their rapid uptake and penetration, autonomous vehicles (AVs) should ensure that occupants are both safe and comfortable. Recent research however revealed that people felt uncomfortable when AVs braked. This may be due to their robot-like braking performance. Existing studies on drivers’ braking behaviour investigated data either from controlled experiments or driving simulators. There is a dearth of research on braking behaviour in normal driving. The objective of this paper is therefore to examine drivers’ braking behaviours by exploiting naturalistic driving data from the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project. On a fixed route of 16.5km long, 16 drivers were asked to drive an instrumented vehicle. A total of about eleven million observations were analysed to identify the profile, value and duration of deceleration events. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The results indicate that the most used profile of the deceleration behaviour follows a hard braking at the beginning when detecting a danger and then becomes smoother. Furthermore, they suggest that the speed, the reason for braking and the deceleration profile mostly affect the deceleration events. Findings from this study should be considered in examining the braking behaviour of AVs.
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