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Pre-collision systems (PCS) use forward-looking sensors to detect the location and motion of vehicles ahead and provide a sequence of actions to help the driver either avoid striking the rear-end of another vehicle or mitigate the severity of the crash. The actions include driver alerts, amplification of driver braking as distance decreases (dynamic brake support, DBS), and automatic braking if the driver has not acted or has not acted sufficiently (crash imminent braking, CIB). Recent efforts by various organizations have sought to define PCS objective test procedures and test equipment in support of consumer information programs and potential certification. This paper presents results and insights from conducting DBS and CIB tests on two production vehicles sold in the US. Eleven scenarios are used to assess the systems' performance. The two systems' performance shows that commercial systems can be quite different.

The effectiveness of Forward Collision Warning (FCW) or similar crash warning/mitigation systems is highly dependent on driver acceptance. If a FCW system delivers the warning too early, it may distract or annoy the driver and cause them to deactivate the system. In order to design a system activation threshold that more closely matches driver expectations, system designers must understand when drivers would normally apply the brake. One of the most widely used metrics to establish FCW threshold is Time to Collision (TTC). One limitation of TTC is that it assumes constant vehicle velocity. Enhanced Time to Collision (ETTC) is potentially a more accurate metric of perceived collision risk due to its consideration of vehicle acceleration. This paper compares and contrasts the distribution of ETTC and TTC at brake onset in normal car-following situations, and presents probability models of TTC and ETTC values at braking across a range of vehicle speeds.

Roadway departure mitigation systems for helping to avoid and/or mitigate roadway departure collisions have been introduced by several vehicle manufactures in recent years. To support the development and performance evaluation of the roadway departure mitigation systems, a set of commonly seen roadside surrogate objects need to be developed. These objects include grass, curbs, metal guardrail, concrete divider, and traffic barrel/cones. This paper describes how to determine the representative color of these roadside surrogates. 24,762 locations with Google street view images were selected for the color determination of roadside objects. To mitigate the effect of the brightness to the color determination, the images not in good weather, not in bright daylight and under shade were manually eliminated. Then, the RGB values of the roadside objects in the remaining images were extracted.

Objective: Opposite-direction crashes can be extremely severe because opposing vehicles often have high relative speeds. The most common opposite direction crash scenario occurs when a driver departs their lane driving over the centerline and impacts a vehicle traveling in the opposite direction. This cross-centerline crash mode accounts for only 4% of all non-junction non-interchange crashes but 25% of serious injury crashes of the same type. One potential solution to this problem is the Lane Departure Warning (LDW) system which can monitor the position of the vehicle and provide a warning to the driver if they detect the vehicle is moving out of the lane. The objective of this study was to determine the potential benefits of deploying LDW systems fleet-wide for avoidance of cross-centerline crashes. Methods: In order to estimate the potential benefits of LDW for reduction of cross-centerline crashes, a comprehensive crash simulation model was developed.

Road departure is a leading cause of fatal crashes in the US and half of all the crashes are related to road departure [1]. Road departure warning (RDW) and road keeping assistance (RKA) are the new active safety areas to be explored. Most of the currently available road-departure detection technologies rely on the detection of lane markings, which are either missing or unclear in many roads. Therefore, in additional to the these lane markings, next-generation road departure detection should rely on the detection of other road edge and boundary objects. Common road edge and boundary indicators include lane marking, grass, curb, metal guardrail, concrete divider, traffic barrels and cones. This paper investigates the distribution of major types of road edges and road boundaries in the United States in order to enhance and evaluate the capabilities and effectiveness of RDW and RKA.

While the number of traffic fatalities as a whole continues to decline steadily over time, the number of pedestrian fatalities continues to rise (up 8% since 2009) and comprises a larger fraction of these fatalities. In 2011 there were 4,432 pedestrians killed and an estimated 69,000 pedestrian injuries [1]. A new generation of Pedestrian Pre-Collision Systems (PCS) is being introduced by car manufactures to mitigate pedestrian injuries and fatalities. In order to evaluate the performance of pedestrian PCS, The Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis is developing a set of test scenarios and procedures for evaluating the performance of pedestrian PCS with the support of the Collaborative Safety Research Center of Toyota. Pedestrian crashes are complex in that there are many aspects about location, driver behavior, and pedestrian behaviors that may have implications for the performance of the PCS.

Road departures are one of the most deadly crash modes, accounting for nearly one third of all crash fatalities in the US. Lane departure warning (LDW) systems can warn the driver of the departure and lane departure prevention (LDP) systems can steer the vehicle back into the lane. One purpose of these systems is to reduce the quantity of road departure crashes. This paper presents a method to predict the maximum effectiveness of these systems. Thirty-nine (39) real world crashes from the National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) database were reconstructed using pre-crash velocities downloaded for each case from the vehicle event data recorder (EDR). The pre-crash velocities were mapped onto the vehicle crash trajectory. The simulations assumed a warning was delivered when the lead tire crossed the lane line. Each case was simulated twice with driver reaction times of 0.38 s and 1.36 s after which time the driver began steering back toward the road.

Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario.

To minimize the risk of run-off-road collision, new technology in Advanced Driver Assistive System (ADAS), called Road Departure Mitigation Systems (RDMS), is being introduced recently. Most of the RDMS rely on clear lane markings to detect road departure events using the camera for decision-making and control actions. However, many roadsides do not have lane markings or clear lane markings, especially in some rural and residential areas. The absence of lane markings forces RDMS to observe roadside objects and road edge and use them as a reference to determine whether a roadway departure incident is happening or not. To support and guide for developing and evaluating RDMS, a testing environment with representative road edges needs to be established. Since the grass road edge is the most common in the US, the grass road edge should be included in a testing environment.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.

According to pedestrian crash data from 2010-2011 the U.S. General Estimates System (GES) and the Fatality Analysis Report System (FARS), more than 39% of pedestrian crash cases occurred at night and poor lighting conditions. The percentage of pedestrian fatalities in night conditions is over 77%. Therefore, evaluating the performance of pedestrian pre-collision systems (PCS) at night is an essential part of the pedestrian PCS performance evaluation. The Transportation Active Safety Institute (TASI) of Indiana University-Purdue University Indianapolis (IUPUI) is conducting research for the establishment of PCS test scenarios and procedures in collaboration with Toyota's Collaborative Safety Research Center. The objective of this paper is to describe the design and implementation of a reconfigurable road lighting system to support the pedestrian PCS performance evaluation for night road lighting conditions.

In recent years Road Departure Mitigation Systems (RDMS) is introduced to the market for avoiding roadway departure collisions. To support the performance testing of the RDMS, the most commonly seen road edge, grass, is studied in this paper for the development of standard surrogate grass. This paper proposes a method for defining the resembling grass color and height features due to significant variations of grass appearances in different seasons, temperatures and environments. Randomly selected Google Street View images with grass road edges are gathered and analyzed. Image processing techniques are deployed to obtain the grass color distributions. The height of the grass is determined by referencing the gathered images with measured grass heights. The representative colors and heights of grass are derived as the specifications of surrogate grass for the standard evaluation of RDMS.

Lane departure warning (LDW) systems can detect an impending road departure and deliver an alert to allow the driver to steer back to the lane. LDW has great potential to reduce the number of road departure crashes, but the effectiveness is highly dependent upon driver acceptance. If the driver perceives there is little danger after receiving an alert, the driver may become annoyed and deactivate the system. Most current LDW systems rely heavily upon distance to lane boundary (DTLB) in the decision to deliver an alert. There is early evidence that in normal driving DTLB may be only one of a host of other cues which drivers use in lane keeping and in their perception of lane departure risk. A more effective threshold for LDW could potentially be delivered if there was a better understanding of this normal lane keeping behavior. The objective of this paper is to investigate the lane keeping behavior of drivers in normal driving.