Shane McLaughlin

A method for evaluating collision avoidance systems using naturalistic driving data

This paper describes a method for use in evaluating the performance of collision avoidance systems (CASs) using naturalistic driving data collected during real crashes and near-crashes. The method avoids evaluation of algorithms against specific assumptions of reaction times or response inputs. It minimizes interpretation of the involved drivers perception and response levels which permits generalizing findings beyond the performance of the involved driver. The method involves four parts: input of naturalistic crash data into alert models to determine when alerts would occur, kinematic analysis to determine when different responses would be required to avoid collision, translation of the time available into an estimate of the percentage of the population able to avoid the specific event, and an evaluation of the frequency of alerts that would be generated by the CASs. The method permits comparison of CAS performance and provides guidance for CAS development. The method is described primarily in the context of Forward Collision Warning CASs, but is applicable to other CAS types.

Driver Measurement: Methods and Applications

This paper presents an overview of methods used when measuring driver behavior and performance. Simulators, test-track, on-road, field-operational-trials, and naturalisitic methods are described. Useful driver measures are described. Three examples are provided of the application of driver measurement in product design and evaluation.

Naturalistic Driving Study: Linking the Study Data to the Roadway Information Database

This report details the methodology used to link the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) data to the SHRP 2 Roadway Information Database (RID), the final critical step in completing the SHRP 2 Safety database. The NDS data set contains extensively detailed data collected continually from more than 5.5 million trips taken by the instrumented vehicles of 3,147 volunteer drivers in six sites. The RID contains extensively detailed data on 25,000 centerline miles of roadways in these six sites, less detailed data on 200,000 centerline miles of roadways in the six states in which the sites were located, and supplemental data on topics such as crash histories, travel volumes, construction, and weather in the six states. The true power of the NDS and the RID comes when they are linked—when each trip is matched to the roadway segments that were traveled and each roadway segment is matched to the trips that traveled on it. The matching methodology documented in this report uses as input the GPS position data collected once per second by the NDS instrumentation and the NAVTEQ network of road segments of all public roads in the continental United States over which the NDS vehicles could travel. The Matching Algorithm associates each GPS point of an NDS trip with the road segment on which a vehicle traveled. The principal challenges overcome by the algorithm were to accommodate GPS readings that may drift far from the correct roadway and to be operationally efficient in comparing the 3.7 billion GPS readings with the 2.6 million NAVTEQ road segments that were traversed. The algorithm’s results are stored in a very large table that associates trip timestamps with road segments.

Exploratory Analysis of Motorcycle Incidents Using Naturalistic Riding Data

This paper introduces the preliminary categorization of motorcycle crash data collected from 100 riders as they rode for a period between 2 months and 2 years. These riders resided in California, Florida, Virginia, and Arizona, and both video and motorcycle kinematic data were collected for every ride. The videos of incidents were reviewed, and the events were described with motorcycle-specific categories for event severity, event nature, incident type, precipitating event, rider reaction, and postmaneuver control. Within the data set of more than 38,000 trips, 22 incidents defined as crashes (involving 18 riders) were identified and categorized. The majority of the crashes (15) were single-vehicle, low-speed crashes. Of the remaining crashes, one was a single-vehicle (higher speed) crash, two were rear-end collisions, and four involved one vehicle turning into or across the path of another at an intersection. Rider response to the precipitating event for more than half of the crashes involved no visible front braking or lateral input. Most crashes occurred within 20 min of the beginning of a trip, during daylight, and in favorable weather conditions. There was no overriding hypothesis implying a relationship of crash occurrence to a specific demographic group in terms of location, age, gender, or motorcycle class. This research applied motorcycle incident categorization to 22 crashes. The categorization terminology and definitions are also applicable to near-crashes. This information is useful in providing an unbiased understanding of what occurs during such incidents and offering a basis for the structured cataloging of crashes, as well as future categorization of near-crashes and crash-relevant events.

Demographics and Personality Factors of Participants in the MSF 100 Motorcyclist Naturalistic Study

This paper describes a 100-rider naturalistic motorcycle study in terms of participant demographics, riding preferences, and behaviors collected through five questionnaires (NEO-FFI-3, Dula Dangerous Driving Index, Frequency of Risky Behavior, Barkley Adult ADHD Rating Scale–IV, and the Motorcycle Safety Foundation Rider Survey). The analysis of the questionnaire data is divided into three bike Types (Touring, Cruiser, Sport). The results indicate that self-reported mileage traveled was highest among Touring bike riders, followed by Sport riders, then Cruiser riders. Differences in Personality factors between Motorcycle Types was generally not found, with the exception of lower levels of Neuroticism in riders of the Touring bikes compared to riders of Cruisers and Sport. The survey based measures indicate that the participant sample does include some measurable heterogeneity in neuroticism scores purely based on questionnaires.

Analysis of Temperature and Precipitation Data in Naturalistic Motorcycle Study

This paper explores differences in the temperature and precipitation in which individuals were found to ride within a large naturalistic riding data set. The data included in this analysis describe approximately 363,000 miles of riding by 98 participants. Trips include travel in over 40 states, as individuals rode for both transportation and pleasure. GPS location data from the motorcycles, combined with historic weather information from the National Oceanic and Atmospheric Administration databases, permitted the investigation of gender, motorcycle type, and installation locations, and their relationship to observed weather conditions during rides. Analyses showed motorcyclists prefer to ride in temperatures between 48°F and 82°F and under dry conditions when possible. If one lives in an area that experiences those temperatures year-round, then it can be expected that trips will continue year-round. If a rider lives in an area with distinctive seasons, those months where the temperature stays within that average range will contain the most rides.