Mike Blommer

Driver Distraction: Evaluation with Event Detection Paradigm

The effects of eight in-vehicle tasks on driver distraction were measured in a large, moving-base driving simulator. Forty-eight adults, ranging in age from 35 to 66, and 15 teenagers participated in the simulated drive. Hand-held and hands-free versions of phone dialing, voicemail retrieval, and incoming calls represented six of the eight tasks. Manual radio tuning and climate control adjustment were also included to allow comparison with tasks that have traditionally been present in vehicles. During the drive the participants were asked to respond to sudden movements in surrounding traffic. The driver’s ability to detect these sudden movements or events changed with the nature of the in-vehicle tasks that were being performed. Driving performance measures such as lane violations and heading error were also computed. The performance of the adult group was compared with the performance of the teenage drivers. Compared with the adults, the teens were found to choose unsafe following distances, have poor vehicle control skills, and be more prone to distraction from hand-held phone tasks.

Evaluation of Lane Departure Warnings for Drowsy Drivers

Lane departure warning (LDW) is a driver warning system designed to reduce the number of unintended lane departures. We addressed warning effectiveness and customer acceptance when the unintended lane departures are the result of drowsy driving. Thirty-two adults who were sleep deprived for 23 hours participated in the study and drove Fords VIRTTEX driving simulator. Four Human Machine Interfaces (HMI) for LDW were evaluated: Steering Wheel Torque, Rumble Strip Sound, Steering Wheel Vibration and Head Up Display. A yaw deviation technique was used to produce controlled lane departures in the first two hours of the drive while for the last 20 minutes driver-initiated lane departures were analyzed. The Steering Wheel Vibration HMI, accompanied by Steering Wheel Torque, was found to be the most effective HMI for LDW in a group of drowsy drivers, with faster reaction times and smaller lane excursions. The Vibration HMI was also perceived by the drowsy drivers to be acceptable and helpful.

Leading Indicators of Drowsiness in Simulated Driving

Drowsiness while driving was measured using three measures: a physiological measure of eye closure, a sustained reaction time task and a subjective assessment. The study was conducted in Fords VIRTTEX driving simulator. Thirty-two adults who were sleep deprived for 24 hours and six adults who had a full night of sleep participated in the study. The performance of the sleep-deprived group was compared with that of the control group. Sleep-deprived drivers had significantly longer PVT reaction times, a greater number of lapses, higher PERCLOS values and perceived themselves as sleepier than did the control group. This study demonstrated the ability to successfully implement drowsiness measures in a driving simulator. The advantage of a three-hour simulator drive in providing increasing levels of drowsiness in each subject was established. These findings provide metrics that can be used to evaluate the efficacy and acceptability of safety systems for drowsy drivers.

Analysing classes of motion drive algorithms based on paired comparison techniques

A paired comparison experiment using 23 subjects was run on the VIRTTEX driving simulator to compare a lane position based motion drive algorithm (MDA) with a classical MDA for a highway speed, lane change manoeuvre. Two different tuning states of the lane position algorithm and four different tuning states for the classical algorithm were tested. The subjective fidelity of the six different motion cases was compared with each other and a Bradley–Terry model was fit to find the fidelity merit of each case. In addition, the driving performance of the subjects for six motion cases was recorded and compared. The motion-tuning cases were selected such that the trade-off in motion quality between overall motion scaling and motion shape distortion (shape-error), as well as the trade-off between lateral specific force and roll-rate motion errors, could be studied. It was found that when the overall scaling is the same, drivers perform better with the lane position algorithm than with the the classical algorithm. A well-tuned, manoeuvre-specific, classical MDA, however, did achieve a subjective fidelity level on a par with the lane position MDA. A generically tuned classical MDA, however, has a significantly reduced fidelity and driving performance when compared with a lane position algorithm with the same scale factor. A strong trade-off between motion shape-errors and overall motion scaling was found. A small increase in motion cue shape-error, combined with an increase in the scale factor from 0.3 to 0.5, led to improved performance and increased subjective fidelity. The results of the experiment also suggest that simulator motion can be improved by reducing the angular-rate shape-error at the expense of the specific force shape-error (while keeping the total normalised shape-error constant).

Effects of Adaptive Lane Departure Warning System on Driver Response to a Surprise Event

A lane departure warning (LDW) system monitors the current lane position of a vehicle and presents a driver alert when one of the vehicles front tires crosses a threshold, for example, the nearest lane line. The primary intent of such warning systems is to prevent or mitigate road departures and related crashes caused by driver distraction or drowsiness. The present evaluation compared adaptive and nonadaptive versions of an LDW system. The adaptive version adapted to the drivers state, whereas the nonadaptive version did not. The adaptive LDW system alerted the driver only if a driver state monitor (DSM) indicated that the driver was looking away from the road ahead for 2 s or longer at about the time when a lane line was crossed. Forty volunteers drove a high-fidelity, moving-base driving simulator in a study to compare driver responses to a surprise lane departure when they used a nonadaptive LDW system and then an adaptive LDW system or vice versa. The results indicated that in the adaptive LDW mode, 13 subjects (34%) either experienced delayed activation of the LDW alert or received no LDW alert at all when they should have, primarily because of both the 2-s rule in the adaptive LDW algorithm and DSM registration issues. The adaptive LDW resulted in significantly larger lane excursions at the onset of the LDW alert compared with those that occurred in the non-adaptive LDW mode. These results highlight the dependence of the performance effects of adaptive systems on system hardware, algorithms, and algorithm parameters.

The Effects of a Scheduled Driver Engagement Strategy in Automated Driving

This simulator study investigated a driver engagement (DE) strategy designed to keep the driver-in-the-loop during automated driving in the face of two different types of secondary tasks. The method, first reported by Carsten et al. (2012), involved driving in fully automated driving mode for 6 minutes followed by 1 minute of manual driving, after which this fixed schedule was repeated several times throughout the drive. This scheduled strategy was compared to a reference condition in which different participants experienced continuous automated driving without interruptions. For each condition, some participants watched a video and others listened to the radio. All drives ended in automated driving mode with a surprise forward collision (FC) hazard to which the participant had to manually intervene. Compared to video watchers, radio listeners responded faster, looked to the road scene more, and they were more often looking forward at FC event onset. The DE strategy had no effect on radio listeners. In contrast, video watchers responded to the hazard more quickly with the scheduled strategy than without it. However, there was no reliable statistical difference between DE conditions in percent-eye-glance-time looking to the forward road scene during automated driving or in the number of drivers looking forward at FC event onset. Thus, the scheduled driver engagement strategy appears beneficial in the face of the visual (video) secondary task, but the reasons for this advantage remain to be determined.

Immediate Recall of Driver Warnings in Forward Collision Warning Scenarios

This paper describes driver recall performance for a forward collision warning (FCW) alert immediately after a critical lead vehicle braking event. A sample of 120 younger and older test participants, balanced by gender, participated in a study of alternative FCW alerts in the Ford Motor Company VIRTTEX driving simulator. A baseline group of participants received no FCW support. Each of the other participants experienced one and only one FCW alert type, visual, auditory, or both. Half of the participants who received an FCW alert had been given knowledge that such a system was in the test vehicle, and half of the participants had been told nothing. A digit-reading distraction task was presented repeatedly during the test drive. The last repetition of this task coincided with the lead vehicle braking event and FCW. Immediately after the event, test participants were asked if they had received a warning and, if so, what they recalled about it. Results were analyzed for those 93 warned test participants who interrupted the digit-reading task to respond to the FCW alert. Approximately 26% of these test participants did not remember receiving a warning at all. Only 58% of the test participants who recollected a warning could accurately recall its modality in all its details, although nearly 90% of those who received the combined audio and video warning recalled at least one of the modalities correctly. Those who received FCW information before the drive had significantly greater recollection than those who were not given any FCW a priori information. Age and gender differences were not statistically significant. The implications of these results are discussed.

Simulator Study of Effects of Alternative Distraction Mitigation Strategies in Driver Workload Manager

This simulator study examined a workload manager developed by Delphi Electronics for the SAVE-IT program and the effects of several different workload mitigation strategies on driver response to a surprise forward collision hazard. The strategies included no in-vehicle task or distraction (baseline); task allowed; task interrupted; and task denied. Forty-eight test participants (24 males and 24 females) between 35 and 55 years of age were randomly assigned in groups of 12 (balanced for gender) to each of the four conditions. Each participant then drove in the Ford VIRTTEX moving-base driving simulator on simulated urban and rural roads and was asked to perform various in-vehicle tasks. During a requested in-vehicle information system task, a vehicle parked on the side of the road would suddenly enter the travel lane, and the drivers response was assessed. Braking response to this critical event indicated no significant differences in mean brake response time as a function of type of mitigation strategy or gender. However, variability in driver responses was significantly less in the task denied condition as compared with the other conditions, possibly because drivers were sensitized to an increased driving demand. Three of 12 test participants in the task interrupted condition showed relatively large brake reaction times attributable to long delays between initial foot motion and braking onset. This delay may indicate an additional delay associated with processing the task interruption and the forward collision warning event itself. Recommendations are provided for further research and for mitigation and driver alerting on the basis of a workload managers assessment of the driving situation.

Sound Quality Metric Development and Application for Impulsive Engine Noise

Many engine tick and knock issues are clearly audible, yet cannot be characterized by common sound quality metrics such as time-varying loudness, sharpness, fluctuation strength, or roughness. This paper summarizes the recent development and application of an objective metric that agrees with subjective impressions of impulsive engine noise. The metric is based on a general impulsive noise model [1], consisting of a psychoacoustic processing stage followed by a transient detection stage. The psychoacoustic stage is extracted from portions of a time-varying loudness model. The primary output of the impulsive engine noise model is a time series that indicates the location and “intensity” of impulsive engine noise events. The information in this time series is reduced either to a single number metric, or to a frequency-based vector of numbers that indicates the amount of impulsiveness in the recorded sound. The frequency-based vector is a distribution of the impulsive engine noise metric as a function of frequency. This is a new development of the impulsive noise model and has proved useful in many applications to indicate which frequency bands contribute most to the overall metric value. An overview of the model and application to various vehicle-level powertrain sounds are presented in this report. Results show distribution of the impulsive engine noise metric as a function of frequency and its agreement with subjective assessments is discussed.

Quantifying customer perceptions of impulsive vehicle noise

Sounds such as spark and diesel knock, squeaks and rattles due to body and suspension components, gear rattle, and other impulsive events, can occur in vehicles and be a major source of customer dissatisfaction. It is desirable to not only know the detection thresholds of these impulsive events, but also the relative annoyance they impart on the customer once they become audible. This work describes research addressing both aspects. The first part of the paper presents a generalized detection model of impulsive events in common vehicle background noises (e.g., wind, road, and powertrain noise). Important properties of the model are the combination of impulsive event information across frequency and also the effect of overall background noise level on detection thresholds. Application of the model to predict detection thresholds for spark knock and also squeaks and rattles is presented. Comparisons are made to measured subjective thresholds. The second part of the paper presents research in objectively qua...