Developing personalised braking and steering thresholds for driver support systems from SHRP2 NDS data

Papazikou, Evita; Thomas, Pete; Quddus, Mohammed

Abstract

Examining the relationships between the factors associated with the crash development enabled the realisation of driver support systems aiming to proactively avert and control crash causation at various points within the crash sequence. Developing such systems requires new insights in personalised pre-crash driver behaviour with respect to braking and steering to develop crash prevention strategies. Therefore, the current study utilises Strategic Highway Research Program 2 Naturalistic Driving Studies (SHRP2 NDS) data to investigate personalised steering and braking thresholds by examining the last stage of a crash sequence. More specifically, this paper carried out an in-depth examination of braking and steering manoeuvres observed in the final 30 s prior to safety critical events. Two algorithms were developed to extract braking and steering events by examining deceleration and yaw rate and another developed and applied to determine the sequence of the manoeuvres. Based on the analysis, thresholds for detecting emerging situations were recommended. The investigation of driver behaviour before the safety critical events, provides valuable insights into the transition from normal driving to safety critical scenarios. The results indicate that 20% of the drivers did not react to the impending event suggesting that they were not aware of the imminent safety critical situation. Future development of Advanced Driver Assistance Systems (ADAS) can focus on individual drivers’ needs with tailored activation thresholds. The developed algorithms can facilitate driver behaviour and safety analysis for NDS while the thresholds recommended could be exploited for the design of new driver support systems.

• Journal Article Type: Article
• Acceptance Date: Jul 14, 2021
• Online Publication Date: Aug 12, 2021
• Publication Date: Sep 30, 2021
• Deposit Date: Jun 5, 2024
• Journal: Accident Analysis and Prevention
• Print ISSN: 0001-4575
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 160
• Article Number: 106310
• DOI: https://doi.org/10.1016/j.aap.2021.106310
• Public URL: https://uwe-repository.worktribe.com/output/12017283