This report presents the results of a research effort in support of a collaborative project aimed at preventing or reducing the severity of vehicle-pedestrian crashes through the use of pedestrian crash avoidance/mitigation (PCAM) systems. PCAM systems use forward-looking detection sensors, typically RADAR and/or cameras, that will detect pedestrians in front of a forward-moving vehicle. PCAM systems warn the driver of an imminent crash with a pedestrian, provide brake assist to the driver, and/or apply automatic braking, to avoid or mitigate the injury severity of vehicle-pedestrian crashes. This analysis is focused on vehicle-pedestrian crashes involving a light vehicle moving forward and contacting a pedestrian in the first harmful event. “Light vehicle” includes any passenger car, van, minivan, sport utility vehicle, or light pickup truck with a gross vehicle weight rating up to 10,000 pounds. The most frequent and fatal pre-crash scenarios were identified through the analysis of national crash databases from the National Highway Traffic Safety Administration during 2005 to 2009. Pre-crash scenarios are prioritized and selected for the development of objective tests to estimate the preliminary system effectiveness of prototype PCAM systems. The following four pre-crash scenarios, in terms of vehicle and pedestrian maneuvers, are recommended to maximize the potential safety benefits of PCAM systems. S1 - Vehicle going straight and pedestrian crossing the road S2 - Vehicle turning right and pedestrian crossing the road S3 - Vehicle turning left and pedestrian crossing the road S4 - Vehicle going straight and pedestrian walking along/against traffic These four recommended pre-crash scenarios resulted in 98 percent of all functional years lost (FYL) and direct economic cost (DEC) of all vehicle-pedestrian crashes, but accounted for only 46 percent of all national cases. The FYL harm measure is a non-monetary value that sums the years of life lost to fatal injury and the years of functional capacity lost to nonfatal injury. The DEC measure includes lost productivity, medical costs, legal and court costs, emergency service costs, insurance administration costs, travel delay, property damage, and workplace losses. Scenario 1 is the most frequent pre-crash scenario and has the highest values for the FYL and DEC measures. Scenarios 2 and 3 address the common vehicle turning scenarios observed in the crash data. Although these scenarios result in less severe injuries, PCAM systems will need to function correctly within these scenarios to help avoid collisions as well as to ensure proper functionality. Scenario 4 has the highest fatality rate and requires PCAM systems to have high-accuracy pedestrian detection that operates at high travel speeds. Crash contributing factors were examined to identify physical settings, environmental conditions, pedestrian characteristics, and other circumstances for the development of objective tests and use as input to the safety benefit estimation methodology. The analysis of physical settings and factors such as vehicle location, pedestrian location, roadway alignment, roadway profile, atmospheric and light conditions, and surface conditions was performed to support the efficiency optimization of PCAM technology by addressing the most common situations. Pedestrian characteristics such as age, gender, and size, along with other contributing factors including traffic flow, number of travel lanes, obstructions, pedestrian direction, and driver and pedestrian physiological conditions, were examined to aid in the development of algorithms to accurately detect pedestrians. Statistics from the crash databases were obtained to aid research of PCAM systems in terms of functionality, operation, applicability, and effectiveness. Based on the analysis of crash databases, the highest frequencies of pedestrian crashes occur: • at speeds of 30 miles per hour or less, • at intersections, • on non-divided roads, • in clear and dry weather, • on dry roads, • during daylight, and • without driver alcohol involvement. Less severe injuries were associated with lower impact speeds, typically at intersections and/or involving vehicle-turning scenarios. The majority of crashes involving fatalities: • occurred at higher impact speeds, • involved pedestrians on the roadway outside of the crosswalk, • occurred at non-junctions, • were associated with darkness, • had pedestrian alcohol involvement, and • involved pedestrians older than 29 years. A safety benefit estimation methodology was presented and exercised as an example of concept, only to the S1 scenario since the baseline crash data, test data results, and results from target population yielded limited samples for the other three scenarios. The potential annual safety benefits were estimated from multiplying the following three values obtained from crash statistics and objective tests of PCAM prototypes: 1. Annual value of harm in the target crash scenario (extracted from crash databases) 2. Ratio of the harm value in the PCAM-applicable crash scenario (i.e., driver of the vehicle did not apply the brakes and the vehicle remained in control prior to striking the pedestrian) over the harm value in the target crash scenario (extracted crash databases) 3. PCAM effectiveness in preventing or reducing the severity of vehicle-pedestrian crashes (derived from objective test data and crash databases) The safety benefit analysis is based on the assumptions that 100 percent of the fleet is equipped with PCAM systems and there is 100-percent system availability and detection without false activations. The system effectiveness is derived only from automatic braking and the analysis assumes that the driver did not apply the brakes prior to impact. Unintended consequences are not considered in the analysis. (Author/publisher)
Abstract