Residual road departure crashes after full deployment of LDW and LDP systems

Abstract

Objective: Road departures are one of the most severe crash modes in the United States. To help reduce this risk, vehicles are being introduced in the United States with lane departure warning (LDW) systems, which warn the driver of a departure, and lane departure prevention (LDP) systems, which assist the driver in steering back to the roadway. Previous studies have estimated that LDW/LDP systems may prevent one third of drift-out-of-lane road departure crashes. This study investigates the crashes that were not prevented, to potentially set research priorities for next-generation road departure prevention systems. Methods: The event data recorder (EDR) data from 128 road departure crashes in the National Automotive Sampling System Crashworthiness Data System (NASS-CDS) from 2011 to 2015 were mapped onto the vehicle trajectory and simulated with LDW/LDP to assess the potential for crash avoidance. The model predicted that 63-83% of single-vehicle road departure crashes may not be prevented by an LDW system and 49% may not be prevented by an LDP system. Results and Conclusions: For LDP systems, which were assumed to have zero latency, no crashes were avoided if the time-to-collision (TTC) from lane crossing to impact was less than 0.55 s. Obstacles such as guardrails and traffic barriers, which tend to be very close to the road, were more common among the remaining crashes. The study shows that LDW/LDP systems are limited by two factors, driver reaction time and TTC to the roadside object. Thus, earlier driver response and longer TTC may help in these situations.

Evidence for the Crash Avoidance Effectiveness of Intelligent and Connected Vehicle Technologies

“…Second, the technical model of ICV technologies in SIM can be adjusted to meet the driver's needs, which helps achieve a better parameter setting to maximize the crash avoidance ability. Riexenger et al focused on the crashes that were not prevented by LDW/LDP in the hope of setting research priorities for next-generation road departure prevention systems [15]. Third, the impact of unexpected factors on the results can be excluded from the SIM study.…”

An Evaluation of the Safety Effectiveness and Cost of Autonomous Vehicles Based on Multivariable Coupling

“…These sensors play different roles when autonomous vehicles run on the road. They can recognize the type, speed, and distance of a nearby object, thus, realizing various safety functions such as AEB, ACC, BSD, LCA, and REB, and therefore avoid accidents to secure the safety of autonomous vehicles [ 14 , 15 ]. V2X has become the autonomous vehicle technology path and development direction to which China has directed significant attention [ 5 , 6 ].…”

“…The effectiveness of LDW is in the range of 10–48% [ 13 ]. The effectiveness of LKA is in the range of 20–51% [ 14 ]. For blind spot detection (BSD), according to the related references, the target crash scenario that could be avoided by BSD is the lane-change crash and the crash avoidance effectiveness of BSD ranges between 14% and 58% [ 15 , 16 ].…”

Predicting occupant head displacements in evasive maneuvers; tuning and comparison of a rotational based and a translational based neck muscle controller

“…For instance, the seated postures and positions of the anthropometric test devices in consumer tests are predefined, and include a limited number of postures and positions [e.g., (Euro NCAP, 2021)]. In real-life, however, initial occupant postures and positions vary (Reed et al, 2020), and vehicle crashes are often preceded by an evasive maneuver (Scanlon et al, 2015;Riexinger and Gabler, 2018;Sander and Lubbe, 2018;Riexinger et al, 2019), leading to greater variability in occupant position (Ólafsdóttir et al, 2013;Kirschbichler et al, 2014;Ghaffari et al, 2018;Reed et al, 2019). To account for this, human body models (HBMs) that include active musculature have been developed.…”