Evaluating Relationships between Perception-Reaction Times, Emergency Deceleration Rates, and Crash Outcomes using Naturalistic Driving Data

Abstract

Perception-reaction time (PRT) and deceleration rate are two key components in geometric design of highways and streets. Combined with a design speed, they determine the minimum required stopping sight distance (SSD). Current American Association of Highway Transportation Officials (AASHTO) SSD guidance is based on 90th percentile PRT and 10th percentile deceleration rate values from experiments completed in the mid-1990s. These experiments lacked real-world distractions, and so forth. Thus, the values from these experiments may not be applicable in real-world scenarios. This research evaluated (1) differences in PRTs and deceleration rates between crash and near-crash events and (2) developed predictive models for PRT and deceleration rate that could be used for roadway design. This was accomplished using (1) genetic matching (with Rosenbaum’s sensitivity analysis) and (2) quantile regression. These methods were applied to the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) data. The analysis results indicated that there were differences in PRT and deceleration rates for crash and near-crash events. The specific estimates were that, on average, drivers involved in crash events took 0.487 s longer to react and decelerated at 0.018 g’s (0.58 ft/s2) slower than drivers in equivalent near-crashes. Prediction models were developed for use in roadway design. These models were used to develop tables comparing existing SSD design criteria with SSD criteria based on the results of the predictive models. These predicted values indicated that minimum design SSD values would increase by 10.5–129.2 ft, dependent on the design speed and SSD model used.

A Simulation-Based Study of the Effect of Brake Light Flashing Frequency on Driver Brake Behavior from the Perspective of Response Time

“…With regard to deceleration rate, previous studies have indicated that different deceleration rates affect the braking response time of following drivers [ 15 , 19 ]. Wood and Zhang [ 27 ] reported that the minimum and maximum deceleration rates among 2971 natural drivers were 0.23 g and 1.09 g, respectively. These represent the minimum and maximum deceleration rates that a driver may experience during normal daily driving.…”

A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure

“…First, the RoI in target spots is split into 12 zones for vehicle (horizontal) and pedestrian (vertical). In our experiment, we set 𝑡 𝑗 as 1, 2, and 3 (after 1 sec, 2 sec, and 3 sec) based on PIEV (perception-intellection-emotion-volition) theory in accident [37]- [40]. PIEV is the amount of time it takes a driver to react to a hazard, and each step is time to discern an object or event, to understand the implications of the situation, to decide how to react, and to initiate the action (engaging the brakes), respectively.…”

“…PIEV is the amount of time it takes a driver to react to a hazard, and each step is time to discern an object or event, to understand the implications of the situation, to decide how to react, and to initiate the action (engaging the brakes), respectively. Much research has been conducted on PIEV time, and designed various time ranges for safe, 1.48 to 2.5 seconds [38]- [40]. ASSHTO (The America Association State Highway and Transportation Officials) recommended to secure 2 till 2.5 seconds [37].…”

Calibrating Design Guidelines using Mental Workload and Reliability Analysis

“…These factors are directly related to MWL and can affect the mean speed and PRT. It is worth noting that Wood et al developed a prediction model that accounts for the dynamic relationship between speed, PRT, and deceleration rate using crash and conflict data (49). This is a new area of research with a huge potential to enrich our understanding of the 'true' failure mechanism.…”

“…Additionally, future work may utilize naturalistic data to enrich the current knowledge of these relationships. These complex relationships will be further complicated if one considers the effect of new technologies such as collision warning systems or as vehicles exhibit an increased level of automation (49).…”