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Lane Departure Warning (LDW) is a production active safety system that can warn drivers of an unintended departure. Critical in the design of LDW and other departure countermeasures is understanding pre-crash driver behavior in crashes. The objective of this study was to gain insight into pre-crash driver behavior in departure crashes using Event Data Recorders (EDRs). EDRs are units equipped on many passenger vehicles that are able to store vehicle data, including pre-crash data in many cases. This study used 256 EDRs that were downloaded from GM vehicles involved in real-world lane departure collisions. The crashes were investigated as part of the NHTSA's NASS/CDS database years 2000 to 2011. Nearly half of drivers (47%) made little or no change to their vehicle speed prior to the collision and slightly fewer decreased their speed (43%). Drivers who did not change speed were older (median age 41) compared to those who decreased speed (median age 27).

Vehicle headlamps and roadway lighting are the major sources of illumination at night. These sources affect contrast - defined as the luminance difference of an object from its background - which drives visibility at night. However, the combined effect of vehicle headlamps and intersection lighting on object contrast has not been reported previously. In this study, the interactive effects of vehicle headlamps and overhead lighting on object contrast were explored based on earlier work that examined drivers’ visibility under three intersection lighting designs (illuminated approach, illuminated box, and illuminated approach + box). The goals of this study were to: 1) quantify object luminance and contrast as a function of a vehicle’s headlamps and its distance to an intersection using the three lighting designs; and, 2) to assess whether contrast influences visual performance and perceived visibility in a highly dynamic intersection environment.

Outdoor objective evaluations form an important part of both tire and vehicle design process since they validate the design parameters through actual tests and can provide insight into the functional performances associated with the vehicle. Even with the industry focused towards developing simulation models, their need cannot be completely eliminated as they form the basis for approving the performance predictions of any newly developed model. An objective test was conducted to measure the ABS performance as part of validation of a tire simulation design tool. A sample vehicle and a set of tires were used to perform the tests- on a road with known profile. These specific vehicle and tire sets were selected due to the availability of the vehicle parameters, tire parameters and the ABS control logic. A test matrix was generated based on the validation requirements.

This study presents a method for determining the time to collision (TTC) at which a driver of the striking vehicle in a real-world, lead vehicle stopped (LVS) rear-end collision applied the brakes. The method employs real-world cases that were extracted from the National Automotive Sampling System / Crashworthiness Data System (NASS / CDS) years 2000 to 2009. Selected cases had an Event Data Recorder (EDR) recovered from the striking vehicle that contained pre-crash vehicle speed and brake application. Of 59 cases with complete EDR records, 12 cases (20%) of drivers appeared not to apply the brakes at all prior to the collision. The method was demonstrated using 47 rear-end cases in which there was driver braking. The average braking deceleration for those cases with sufficient vehicle speed information was found to be 0.52 g's. The average TTC that braking was initiated at was found to vary in the sample population from 1.1 to 1.4 seconds.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.