Philip Pidgeon

A Customizable Human/Vehicle Interface for Enhanced Operator Performance

The emergence of cost effective electronics and actuators within the transportation industry allows the presentation of increased driver feedback for greater situational awareness. The operator feedback channels can be broadly divided into visual, audio, and haptic. To date, the automotive community has primarily relied on instrument panel lamps and buzzer/chime sounds to notify the driver of important information while the vehicle’s interaction with the road is mechanically communicated through the steering wheel “feel” and the driver seat motion. However, an opportunity exists to integrate the visual, audio, and haptic feedback channels in a more effective manner to increase driver safety. For instance, the driver may receive haptic driving information through high frequency and low amplitude steering wheel vibrations. Visual feedback may be presented in the form of LED lights on the dashboard and instrument cluster. Similarly, audio messages that are recognized through a different cognitive process than visual and haptic signals may be integrated into the cockpit. In this paper, a comprehensive approach is proposed for driver communication through visual, audio, and haptic feedback. Laboratory tests have been conducted with human subjects using a custom driving simulator to evaluate driver notification strategies. The effectiveness of each feedback channel is evaluated and the results demonstrate that the coordinated presentation of vehicle operational data through targeted feedback channels increase the operator’s overall safety.Copyright

Validation of a fixed-base automotive simulator for run-off-road safety and recovery training

Traffic fatalities and injuries continue to demand the attention of researchers and governments across the world as they remain significant factors in public health and safety. Enhanced legislature, together with vehicle and roadway technology, has helped to reduce the impact of traffic crashes in many scenarios. However, one specifically troublesome area of traffic safety which persists is the run-off-road crash where a vehicle’s wheels leave the paved portion of the roadway and begin to travel on the shoulder or side of the road. Large percentages of fatal and injury traffic crashes are attributed to run-off-road events. One of the most critical reasons why run-off-road scenarios quickly evolve into serious crashes is poor driver performance. Drivers are unprepared to handle the situation safely and often execute dangerous maneuvers, such as overcorrection, which can lead to devastating results. One countermeasure which directly addresses the driver performance is driver education and training. In this article, a simulator-based driving environment is proposed specifically for run-off-road recovery training. A human subject study is used to validate the simulator as an effective tool for replicating the run-off-road experience with the additional benefit of receiving insight into driver reactions to run-off-road events. Analysis of variance (ANOVA) results of subjective questionnaire data and objective performance evaluation parameters show strong correlations to run-off-road crash data and previous run-off-road study conclusions. In particular, higher vehicle velocities, curved roads, and larger differences between the friction coefficient of the road and the friction coefficient of the shoulder all negatively impacted drivers’ recoveries from run-off-road scenarios. The only non-significant impact found was that of the roadway edge, indicating a possible limitation of the simulator system with respect to that particular environment variable. The validation study provides a foundation for further evaluation and development of a simulator-based run-off-road recovery training program to help to equip drivers with the skills to recognize and recover safely from this dangerous and often deadly scenario.

A Mobile Tailgating Detection System for Law Enforcement Surveillance

A number of automotive crashes occur each year due to semitrailers following passenger vehicles too closely on interstate highways and secondary roads. This hazardous practice, called tailgating, accounted for over 40% of the 110,000 trailer-passenger vehicle crashes recorded by the National Highway Traffic Safety Administration (NHTSA) in 2010. Tailgating is difficult to detect and document using visual methods and law enforcement agencies must depend on trained officers, whose abilities may be limited. In this paper, a proposed tailgating detection system, mounted to the officer’s patrol vehicle, continuously monitors both passenger and commercial vehicles, as the officer travels down the roadway. A rotating laser range-finding sensor feeds information to a microprocessor that continuously searches for the occurrence of tailgating. A weighting algorithm determines when a tailgating event has definitively occurred to reduce system sensitivity. If an event is detected, the officer is notified with audio and visual cues. A time stamped record including all relevant system information for later use in legal prosecution is also produced. In a virtual case study, the computer generated roadway environment was populated with vehicles of varying velocity and location. The numerical results show that the detection algorithm was able to successfully locate all of the virtual vehicles and accurately determine tailgating events under a number of different simulation conditions.Copyright

Automotive Participant Tailgating Safety Training Device: Design and Test

The safe operation of a ground vehicle requires a combination of driver skills and behaviour, motor–vehicle knowledge, and recognition of driving conditions and environments. One dangerous scenario commonly encountered by drivers is tailgating. In this paper, a lightweight tailgating device that can be installed on a sport utility vehicle (or truck) to support driver training activities will be presented. The tailgating apparatus has been field tested on a closed course as part of a safe driving programme. Objective vehicle measurements and subjective instructor evaluations revealed that 75% of students successfully completed the driving task at a passing level.