Wassim G. Najm

DRIVER/VEHICLE CHARACTERISTICS IN REAR-END PRECRASH SCENARIOS BASED ON THE GENERAL ESTIMATES SYSTEM (GES)

This paper studies different driver and vehicle characteristics as they impact pre-crash scenarios of rear-end collisions. It gives a statistical description of the five most frequently occurring rear-end precrash scenarios based on vehicle and driver characteristics. Variables were drawn from the General Estimates System (CRASH) crash database. Results from the study can be applied towards estimating the safety benefits of advanced technology rear-end collision avoidance systems for reducing the number of crashes and mitigating the level of severity.

ESTIMATION OF CRASH INJURY SEVERITY REDUCTION FOR INTELLIGENT VEHICLE SAFETY SYSTEMS

A novel methodology is presented to estimate the safety benefits of intelligent vehicle safety systems in terms of reductions in the number of collisions and the number and severity of crash-related injuries. In addition, mathematical models and statistics are provided to support the estimation of the crash injury reduction factor in rear-end, lane change, and single vehicle roadway departure collisions. Statistics on the distribution of vehicle types and weights in the United States are supplied. Moreover, mathematical equations are derived to estimate the average harm per collision. Finally, statistics on the average harm per occupant are obtained from the 1994 and 1995 Crashworthiness Data System crash databases. (A) For the covering abstract see ITRD E106371.

FEASIBILITY OF DRIVER JUDGMENT AS BASIS FOR A CRASH AVOIDANCE DATABASE

A crash avoidance database structure that is based on driver judgments is proposed. The structure comprises four driving conflict states (low risk, conflict, near crash, and crash) that correspond with advisory warning, crash-imminent warning, and crash mitigation counter-measures. The feasibility of this database structure is investigated by answering two questions: (a) Can the driving states be reliably quantified? and (b) Can the quantified states be used to create a useful crash avoidance database? The feasibility discussion centers on a specific dynamic scenario that involved braking maneuvers by a following vehicle to avoid a rear-end crash with a stopped lead vehicle. The quantification of driver judgment data from a controlled test track study is discussed as a foundation to identify rough quantitative locations for the conflict and near-crash state transitions, and crash data from a driving simulator experiment are used to estimate the crash state boundary. A database of on-road, naturalistic driving data is compared with the controlled experiments to evaluate the results. The method is found to be feasible, and recommendations for further development are presented.

Modeling driver response to lead vehicle decelerating

This paper presents a driver performance map of braking and steering in response to three driving scenarios that lead to rear-end crashes. This map encompasses low risk, conflict, near-crash, and crash imminent driving states that correspond to advisory warning, crash imminent warning, and crash mitigation functionalities for intelligent vehicle rear-end crash countermeasures. Specifically, this paper models driver response to a lead vehicle decelerating by building upon prior research that estimated the state boundaries for driver response to lead vehicle stopped or moving at slower constant speed. In addition, this paper compares braking performance to steering performance in the lead vehicle-decelerating scenario using plots of range and range-rate that roughly quantify the boundaries between the driving conflict states. Driver performance is also discussed among the three rear-end crash scenarios.

DEFINITION OF THE LIGHT VEHICLE OFF-ROADWAY CRASH PROBLEM FOR THE INTELLIGENT VEHICLE INITIATIVE

Off-roadway crashes involving light vehicles (passenger cars, sport utility vehicles, vans, and pickup trucks) were analyzed based on the 1998 General Estimates System crash database to support the development of countermeasure systems as part of the U.S. Department of Transportation’s Intelligent Vehicle Initiative. An off-roadway crash occurs when a vehicle in transport departs the road due to loss of control or crossing the edge of the roadway. Approximately 924,000 such crashes occurred in the United States in 1998. These crashes are grouped into six distinct precrash scenarios (3 × 2 matrix) based on vehicle movements (going straight, negotiating a curve, or initiating a maneuver) and critical events (departed roadway edge or lost control). These scenarios are described statistically in terms of their physical setting, which consists of the roadway type, land use, relation to junction, number of travel lanes, and speed limit. Moreover, factors that might have contributed to the cause of these crashes are identified. According to this analysis, 85 percent of these off-roadway crashes occurred on nonfreeways, 64 percent in rural areas, 83 percent away from junctions, 60 percent on two lanes of travel, and 62 percent below the 89-km/h (55-mph) speed limit. In addition, speeding and alcohol were reported in 25 and 20 percent of these crashes, respectively. Finally, inclement environmental conditions or driver inattention or distraction might have contributed to about 42 percent of these crashes.

Breakdown of Light Vehicle Crashes by Pre-Crash Scenarios as a Basis for Countermeasure Development

This paper presents a statistical description of light vehicle (passenger vehicles, sports utility vehicles, vans, and pickup trucks) crashes in terms of their major crash types, physical setting, and concomitant pre-crash scenarios based on the 2000 General Estimates System national crash database. This database contains variables that categorize the pre-crash situation and identify the vehicle movements and critical events immediately prior to the collision. This crash analysis supports the U.S. Department of Transportations Intelligent Vehicle Initiative to solve traffic safety problems through the development and deployment of vehicle-based crash avoidance systems using advanced technologies. In the year 2000, light vehicles were involved in about 6,133,000 or 96.0% of all police-reported crashes on U.S. roadways. A breakdown of these crashes revealed 10 different crash types. Rear-end, crossing paths, off-roadway, and lane change crash types comprised the majority of these crashes with a combined frequency of 5,240,000 crashes. About 39.6% of all light vehicle crashes occurred at non-junctions, while 24.5% and 20.4% of all light vehicle crashes happened respectively at intersections and near intersections. Further breakdown of the four most dominant crash types identified 25 dynamically distinct pre-crash scenarios that influence the design of the sensory and decision-making elements for potential countermeasure systems.

Identification of Traffic States From Onboard Vehicle Sensors

This paper describes an algorithm that identifies the state of traffic ahead of a moving vehicle using onboard sensors. This algorithm approximates the level of service as defined in the Highway Capacity Manual, which portrays a range of traffic conditions on a particular type of roadway facility. The traffic state forms an independent variable in an evaluation plan to assess the benefits and capability of an automotive rear-end crash avoidance system in a field operational test. The algorithm utilizes inputs from vehicle sensors, onboard radar, global positioning system, and digital map to classify the traffic ahead into light, medium, and heavy states. Basically, the algorithm segregates the roadway into four different categories based on the road type (freeway or non-freeway), posted speed limit, and traffic flow conditions. In addition, the algorithm computes two key parameters: (1) number of vehicles in the radar field of view that are moving in the same direction as the test vehicle, and (2) speed ratio between the vehicle travel speed and the posted speed limit. A logical process that ties these two parameters to each of the four roadway categories then determines the traffic state. The conditions of the logical process were optimized by minimizing the error between the calculated and observed traffic states using samples of video and numerical data collected from an instrumented vehicle on public roads.

Safety benefits estimation of an intelligent cruise control system using field operational test data

The potential safety benefits of an Intelligent Cruise Control (ICC) system are assessed in terms of the number of rear-end crashes that might be avoided on US freeways if all vehicles were equipped with such a system. This analysis utilizes naturalistic driving data collected from a field operational test that involved 108 volunteers who drove ten passenger cars for about 68 and 35 thousand miles in manual and ICC control modes, respectively. The effectiveness of the ICC system is estimated at about 17 percent based on computer simulations of two rear-end precrash scenarios that are distinguished by whether the following vehicle encounters a suddenly-decelerating or slow-moving lead vehicle. The ICC system has the potential to eliminate approximately 13 thousand police-reported rear-end crashes on US freeways, using 1996 national crash statistics. (A) For the covering abstract see IRRD E102826.

Naturalistic Study of Truck Following Behavior

A baseline case was created for the following behavior of heavy-truck drivers with the use of naturalistic driving data to support the development of automated platooning. A truck platoon is a string of trucks following each other in the same lane at short distances. Grouping vehicles in platoons can increase capacity on roads, save significant fuel, reduce emissions, and potentially result in improved safety. However, these benefits can be realized only if the platoons operate in an automated, coordinated manner. Because little literature of truck following behavior exists to support the development of such truck platoons, this research focused on how closely trucks follow other vehicles on highways under various environmental conditions, how closely a truck follows a leading vehicle when other vehicles cut in between, and the safety impact of following at different headways. Findings indicate that trucks follow other vehicles at an average headway of about 2 s overall, and those headways are shorter when following a passenger car rather than a heavy truck, on state highways rather than on Interstates, in clear weather rather than in rain or snow, and during the day rather than during at night. Vehicles usually do not cut in when a truck is following another vehicle at less than 25-m (82-ft) or 1.0-s headway. For manual response times, the rear-end crash risk increases considerably at headways of less than 1.0 s; for automated response times, crash risk is almost negligible at headways as low as 0.5 s.

Effects on Drivers’ Behavior of Forward Collision Warning System Alerts:

Forward collision warning (FCW) systems have been available for some time. Drivers 20-29 years of age are especially at risk for being involved in motor vehicle crashes due to distraction. (FCW) systems could potentially reduce rear-end crashes caused by distraction and other factors. A field operational experiment was run to determine whether FCW systems could reduce over time for this cohort the driving conflicts (rear-end crashes and near crashes) with lead vehicles. Information was gathered on 38 drivers over varying periods of time (up to a year) on alert rates [over vehicle miles traveled (VMT) or months], conflict rates, and responses to conflicts. Conflicts per 1,000 VMT decreased by 76.6%. The decrease in conflict rates was correlated with the decrease in alert rates (0.71), suggesting that drivers were responding positively to the alerts. There was no change in the responses to conflicts. Females had many fewer alerts than males.