Effectiveness Of Driver Assistance Systems Research Articles

Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology.

VEHICLE–PEDESTRIAN INTERACTIONS INTO AND OUTSIDE OF CROSSWALKS: EFFECTS OF DRIVER ASSISTANCE SYSTEMS

This study aimed to analyse the driver’s behaviour during the interaction with a pedestrian crossing into and outside the zebra crossing, and evaluate the effectiveness of two kinds of Advanced Driver Assistance System (ADAS) that provided to the driver an auditory alert, and a visual alert to detect the pedestrian. 42 participants joined the experiment conducted using the fixed-base driving simulator of the Department of Engineering (Roma Tre University). They experienced different crossing conditions (legal and illegal) and ADAS conditions (no ADAS, visual warning and auditory warning) in an urban scenario. The parameters Time-To-Arrive (TTA) and Speed Reduction Time (SRT) were obtained from the drivers’ speed profiles in the last 150 m in advance of the conflict point with the pedestrian. Results clearly showed the criticality of illegal crossings. When the pedestrian crossed outside of the crosswalk, the highest number of collision occurred and the ANalysis Of VAriance (ANOVA) returned significant effects on both the dependent variables TTA and SRT, highlighting the higher criticality of the vehicle–pedestrian interaction and the more abrupt yielding manoeuvre. Positive effects (the vehicle–pedestrian interaction was less critical and the yielding manoeuvre was smoother) emerged for both the driver assistance systems, although not statistically significant. Besides, both the driver assistance systems positively affected the behaviour of the average cautious drivers. No significant effects of the warning systems were recorded on the aggressive drivers, which because of their behavioural characteristics ignored the warning alarm. In addition, no significant effects of the warning systems were recorded for the very cautious drivers, which adjusted their behaviour even before the alarm trigger. Finally, the outcomes of the questionnaire submitted to the participants highlighted the clear preference for the auditory warning, probably because of the different physical stimuli that are solicited by the warning signal. The results confirm that adequate pedestrian paths should be planned to avoid jaywalker conditions, which induce the driver to assume critical driving behaviour and provide useful findings of the effectiveness of driver assistance systems for pedestrian detection.

Development of an Augmented Reality Vehicle for Driver Performance Evaluation

Observing drivers' behaviors by reproducing traffic accidents and conflict situations is important for developing advanced driver assistance systems. For this purpose, driving simulators are frequently used to evaluate the effectiveness of driver assistance systems during product development. However, motion (simulator) sickness can be a serious practical problem with driving simulators. Therefore, an instrumented vehicle, the JARI-ARV (Japan Automobile Research Institute-Augmented Reality Vehicle), was developed to reproduce realistic traffic accident and conflict scenarios without endangering the driver. In this study, we examined level of control in the following cases: a right turn and encounter with a pedestrian by comparing the JARI-ARV with a standard (unaltered) same model vehicle. Results of the experiment indicated that drivers tend to react to virtual traffic participants in the same way as driving a standard vehicle. The study indicates that the JARI-ARV can play a useful role in human factors research.

Evaluation of Safety Effects of Driver Assistance Systems through Traffic Simulation

Road safety is a major concern in all countries, and large efforts are constantly dedicated to create safer traffic environments. Today increasing attention is turned toward active safety improving countermeasures that improve road safety by reducing accident risks. Such active countermeasures include advanced driver assistance systems (ADAS). To ensure that these new applications result in real safety improvements, a priori estimations of safety effects are needed. This paper considers estimation of the safety effects of ADAS through traffic simulation. Requirements imposed on a traffic simulation model to be used for ADAS evaluation are presented, and a car-following model to be used in simulations that include ADAS-equipped vehicles is proposed. ADAS have an impact on traffic through the functionalities of ADAS and through changes in driver behavior for ADAS-equipped vehicles. Driver behavior for ADAS-equipped vehicles has usually not been considered in previous simulation studies, including those for ADAS-equipped vehicles. Simulation runs of rural road traffic that used the proposed car-following model did, however, indicate that behavioral changes caused by the ADAS were important factors for the safety impact. Modeling of the behavior of drivers in ADAS-equipped vehicles is therefore essential for reliable conclusions on the road safety effects of ADAS.

Sensor-Friendly Vehicle and Roadway Cooperative Safety Systems: Benefits Estimation

An analysis was performed to estimate the potential national costs and benefits of cooperative vehicle and roadway measures to enhance the effectiveness of driver assistance systems. These cooperative measures—query-response communication systems, light-emitting-diode brake light messaging, radar cross-section paint-striping modifications, fluorescent paint for lane and other marking applications, passive amplifiers on license plates, spatial tetrahedral arrays of reflectors, and in-vehicle corner cubes—are briefly described, along with assumptions that were made regarding performance. For the example lane departure case, the incremental nationwide effectiveness over an autonomous collision-avoidance system is estimated and monetized. This was generally determined with respect to annual crash-reduction savings, although the technique used allows other mobility benefits to be considered. The marginal benefits of providing each sensor-friendly technology were then calculated and aggregated across the various Intelligent Vehicle Initiative services so that a total marginal benefit was determined for each technology. Complementing this, a method has been established to estimate the magnitude of at- and near-intersection lead-vehicle-not-moving crashes for these technologies. Together, these methods illustrate national benefits across all crash types (the three-step process) and a more focused means to estimate benefits for a particular crash type (rear-end collisions at or near intersections)—and provide a composite approach to the problem.