Field Operational Test Data Research Articles

Exploring the associations between driving volatility and autonomous vehicle hazardous scenarios: Insights from field operational test data

With the promising development and deployment trends of autonomous vehicles (AVs), AVs’ operation safety has become a key issue worldwide. Studies have been conducted to reveal the risk factors of AV operation safety based upon AV-involved crash reports. However, the crash data sample size was limited and the crash reports only recorded static information, thus it failed to identify crash contributing factors and further provide feedbacks to AV algorithm development. In this study, the risk factors were investigated based upon hazardous scenarios, which were claimed to possess consistent causal mechanisms with crash events. First, contributing factors were extracted from both vehicle kinematics and traffic environment aspects, and their volatility features were obtained. Then, path analysis models were developed to reveal the concurrent relationships between scenario volatility and hazardous scenario occurrence probability. Besides, to understand the varying risk factors for hazardous scenarios caused by human drivers and AVs, a logit regression model was further established. The modeling results showed that large volatility of space headway held direct impacts on increasing the AV driving risks. And the volatility of the drivable road area had no significant impacts on AV driving risks while it indirectly influenced human driving risks. Finally, result implications for AV driving behavior improvements have been discussed.

The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data

Deceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management.

Using Naturalistic Field Operational Test Data to Identify Horizontal Curves

Investigations to identify relationships between crashes and road features usually deal with effects of only one or two of the main components of traffic safety, i.e., human, vehicle, and infrastructure performance. There are several contributing factors of the components that together lead to a crash. This study devises an approach to include information from all three components in a system using field operational test (FOT) data. FOT data are recorded from real-life driving that is different from traffic simulations and specific on-site data collection. The study focuses on identifying horizontal curves using FOT and provides access to vehicle and human response data at the exact time when the vehicle drove in a specific location. A method has been developed to derive path radius and to identify start-end points of horizontal curves using FOT data. With this information, vehicle response signals and human behavior data can then be arranged on a common axis referenced to the curve. The approach also identifies lane changing maneuvers on curves that can be used to evaluate potential crash triggers. The application of this method allows for reviewing changes in the regulatory speed limit, curve geometry, or crash history and thus evaluates the design of curves and choosing appropriate countermeasures.

Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets

Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10 000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task.

Assessing Contextual Factors That Influence Acceptance of Pedestrian Alerts by a Night Vision System

We investigated five contextual variables that we hypothesized would influence driver acceptance of alerts to pedestrians issued by a night vision active safety system to inform the specification of the system's alerting strategies. Driver acceptance of automotive active safety systems is a key factor to promote their use and implies a need to assess factors influencing driver acceptance. In a field operational test, 10 drivers drove instrumented vehicles equipped with a preproduction night vision system with pedestrian detection software. In a follow-up experiment, the 10 drivers and 25 additional volunteers without experience with the system watched 57 clips with pedestrian encounters gathered during the field operational test. They rated the acceptance of an alert to each pedestrian encounter. Levels of rating concordance were significant between drivers who experienced the encounters and participants who did not. Two contextual variables, pedestrian location and motion, were found to influence ratings. Alerts were more accepted when pedestrians were close to or moving toward the vehicle's path. The study demonstrates the utility of using subjective driver acceptance ratings to inform the design of active safety systems and to leverage expensive field operational test data within the confines of the laboratory. The design of alerting strategies for active safety systems needs to heed the driver's contextual sensitivity to issued alerts.

A field operational test on valve-regulated lead-acid absorbent-glass-mat batteries in micro-hybrid electric vehicles. Part I. Results based on kernel density estimation

In March 2007 the BMW Group has launched the micro-hybrid functions brake energy regeneration (BER) and automatic start and stop function (ASSF). Valve-regulated lead-acid (VRLA) batteries in absorbent glass mat (AGM) technology are applied in vehicles with micro-hybrid power system (MHPS). In both part I and part II of this publication vehicles with MHPS and AGM batteries are subject to a field operational test (FOT). Test vehicles with conventional power system (CPS) and flooded batteries were used as a reference. In the FOT sample batteries were mounted several times and electrically tested in the laboratory intermediately. Vehicle- and battery-related diagnosis data were read out for each test run and were matched with laboratory data in a data base. The FOT data were analyzed by the use of two-dimensional, nonparametric kernel estimation for clear data presentation.The data show that capacity loss in the MHPS is comparable to the CPS. However, the influence of mileage performance, which cannot be separated, suggests that battery stress is enhanced in the MHPS although a battery refresh function is applied. Anyway, the FOT demonstrates the unsuitability of flooded batteries for the MHPS because of high early capacity loss due to acid stratification and because of vanishing cranking performance due to increasing internal resistance. Furthermore, the lack of dynamic charge acceptance for high energy regeneration efficiency is illustrated. Under the presented FOT conditions charge acceptance of lead-acid (LA) batteries decreases to less than one third for about half of the sample batteries compared to new battery condition. In part II of this publication FOT data are presented by multiple regression analysis (Schaeck et al., submitted for publication [1]).

A field operational test on valve-regulated lead-acid absorbent-glass-mat batteries in micro-hybrid electric vehicles. Part II. Results based on multiple regression analysis and tear-down analysis

In the first part of this work [1] a field operational test (FOT) on micro-HEVs (hybrid electric vehicles) and conventional vehicles was introduced. Valve-regulated lead-acid (VRLA) batteries in absorbent glass mat (AGM) technology and flooded batteries were applied. The FOT data were analyzed by kernel density estimation. In this publication multiple regression analysis is applied to the same data. Square regression models without interdependencies are used. Hereby, capacity loss serves as dependent parameter and several battery-related and vehicle-related parameters as independent variables. Battery temperature is found to be the most critical parameter. It is proven that flooded batteries operated in the conventional power system (CPS) degrade faster than VRLA–AGM batteries in the micro-hybrid power system (MHPS).A smaller number of FOT batteries were applied in a vehicle-assigned test design where the test battery is repeatedly mounted in a unique test vehicle. Thus, vehicle category and specific driving profiles can be taken into account in multiple regression. Both parameters have only secondary influence on battery degradation, instead, extended vehicle rest time linked to low mileage performance is more serious.A tear-down analysis was accomplished for selected VRLA–AGM batteries operated in the MHPS. Clear indications are found that pSoC-operation with periodically fully charging the battery (refresh charging) does not result in sulphation of the negative electrode. Instead, the batteries show corrosion of the positive grids and weak adhesion of the positive active mass.

Method for Identifying Vehicle Movements for Analysis of Field Operational Test Data

The independent evaluation of a roadway-departure crash-warning system will involve identifying and classifying conflicts and near-collisions from a vast quantity of field data. The classification of these events includes identifying vehicle movements such as negotiating a curve, making a turn, changing lanes, and merging. A unified algorithm that relies solely on vehicle kinematic data is developed for identifying such movements. The method works reliably for various types of road surfaces, road types, speeds, and conditions. The algorithm relies on metrics such as heading angle, lateral position, radius of curvature, and peak yaw rate—calculated from yaw rate and vehicle speed data—to classify vehicle-movement events from a large file of vehicle data. Tuning parameters for the algorithm, set both analytically and empirically, are provided. The results of the tests show that the algorithm makes correct identifications of vehicle movement about 80% of the time. The highest success is for turns, followed by curves and lane changes. Although these numbers improve on previous methods, there is additional room for improvement that gives impetus for future work.