Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data. (November 2019)

• Record Type: Journal Article
• Authors: Fleming, James M.; Allison, Craig K.; Yan, Xingda; Lot, Roberto; Stanton, Neville A.
• Is Part Of: Safety science. Volume 119(2019)
• Page Start: 76
• Page End: 83
• Publication Date: 2019-11
• Subjects: ADAS -- Speed choice -- Safe cornering -- Car following -- Driver modelling -- Naturalistic driving; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2018.08.023
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: Naturalistic data collected in a small-scale study to evaluate driver models. Following models with preferred headways show a poor fit to naturalistic data. Following models based on limits on time-to-collision perform better in practice. Cornering models with lateral acceleration bounds show good fits to real-world data. Implications for design of driver assistance systems are discussed. Abstract: Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future research.