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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.

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.

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.

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.

Road Departure Mitigation System (RDMS) is a new feature in vehicle active safety systems. It may not rely only on the lane marking for road edge detection, but other roadside objects This paper discusses the radar aspect of the RDMS testing on roads with grass road edges. Since the grass color may be different at different test sites and in different seasons, testing of RDMS with real grass road edge has the repeatability issue over time and locations. A solution is to develop surrogate grass that has the same characteristics of the representative real grass. Radar can be used in RDMS to identify road edges. The surrogate grass should be similar to representative real grass in color, LIDAR characteristics, and Radar characteristics. This paper provides the 24 GHz and 77 GHz radar characteristic specifications of surrogate grass.

Based on RADAR and LiDAR measurements of deer with RADAR and LiDAR in the Spring and Fall of 2014 [1], we report the best fit statistical models. The statistical models are each based on time-constrained measurement windows, termed test-points. Details of the collection method were presented at the SAE World Congress in 2015. Evaluation of the fitness of various statistical models to the measured data show that the LiDAR intensity of reflections from deer are best estimated by the extreme value distribution, while the RCS is best estimated by the log-normal distribution. The value of the normalized intensity of the LiDAR ranges from 0.3 to 1.0, with an expected value near 0.7. The radar cross-section (RCS) varies from -40 to +10 dBsm, with an expected value near -14 dBsm.

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.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

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.