Driver Assistance Technologies Research Articles

An assistance technology towards maintaining efficient driving strategies of electric vehicle

ABSTRACT In order to overcome the problems of electric vehicle (EV) range anxiety and inadequate charging station, a driver assistance technology is presented here. The proposed technology assists a driver to maintain an efficient driving strategy (EDS) that minimizes energy consumption toward increasing the driving range. Additionally, it provides an anticipated range of EV corresponding to its current battery charge which alerts the driver to take a decision for required charging. EDS is determined by solving a multi-objective optimization problem corresponding to the current route characteristics. Optimization problem considers three primary objectives, minimization of energy consumption and travel time and maximize journey comfort, and follows an EV drive model. Effectiveness of the proposed technology is identified by analyzing the effect of adopting EDS in a micro-trip planning with an E-bus based on experimental drive data. A drive model for the E-bus is formulated and validated through comparative analysis of speed, motor current, and voltage that are obtained in simulation and experimentation. Such analysis was performed independently in four micro-trips identified from E-bus drive data following a typical route. It was found that the driver assistance technology reduces both energy consumption and travel time than that found by following a random driving strategy.

Stability of Adaptive Cruise Control of Automated Vehicle Platoon under Constant Time Headway Policy

Traffic congestion is becoming more prevalent as the number of vehicles on the roads continues to rise.To shorten travel times and enhance driver comfort, a range of Advanced Driver Assistance Systems (ADAS) has been developed to assist drivers in urban areas and on highways. The demand for increasing road capacity has introduced the concept of vehicle platooning, where the use of the Adaptive Cruise Control (ACC) system is a key function within the advanced driver assistance (ADAS) technology, this technology manages the vehicle's longitu-dinal control in specific driving conditions. Cars equipped with ACC can efficiently maintain a set distance from the vehicle ahead, easing the driver’s workload while offering advantages such as improved road capacity, lower fuel consumption, and reduced pollution emissions. However, they can be susceptible to string instability, resulting in the amplification of oscillations caused by speed variations along the platoon's rear. This paper presents a string stability analysis of car platoons equipped with ACC system based on a heuristic method by choosing of the constant time headway policy (CTHP). The constant time headway policy selection for string stability is based on the Nyquist diagram of the transfer function of the spacing errors between two cars. A platoon operated by using distance-based ACC control structures is implemented. These structures employ a linear quadratic regulator (LQR) using a dual integrator. The simulation results were acquired by modeling and simulating the studied platoon within Matlab/Simulink.

Neuro-Adaptive AI for Dynamic Distraction Mitigation in Autonomous Vehicle Environments

As autonomous vehicles develop, driver distraction becomes even more crucial as it affects both safety and operational efficiency. In this work, we investigate the gamut of new AI tools for combating and processing visual distraction scenarios within autonomous vehicles. This includes AI-based driver monitoring systems to determine the level of attention, visual distraction classification with deep learning models, augmented reality head-up displays for focal projection of critical information and gesture/voice-controlled interfaces are used in order to reduce visual interactions. This also includes how predictive analytics; adaptive user interfaces and personalized distraction mitigation programs will see AI improve driver focus and thus safety. These advanced systems are designed to provide a safer and more efficient driving experience in the emerging era of autonomous capabilities by leveraging the scalability of advanced driver-assistance technologies.

A journey from users' experience to their expectations in the realm of future advanced driver assistance systems

ABSTRACT Perhaps the most difficult aspect of implementing an advanced driver assistance system into a vehicle is making it fully customizable. Several driver-specific customization challenges must be met in the cabin. Comfort, variety, and individualized organization of the user's possessions are all within the reach of some systems. Advanced driver assistance systems were the focus of this study, specifically the user's interface with the vehicle. To gauge current knowledge and anticipations for future driver assistance technologies, we administered questionnaires and conducted user-focused interviews with 38 individuals across India. The study's limited scope did provide a few interesting avenues for further investigation. The results of the study reveal that users have demonstrated a larger dependence on advanced driver assistance systems (ADAS) in terms of safety considerations in relation to the current driving experience. In addition, they are particularly interested in the numerous in-vehicle facility features that are available with existing ADAS, and at the same time, they are looking forward to having personalized ADAS for their fleet in the future.

Teaching advanced technology (ADAS) and use of touch screens in driver training in Norway

As many as 4,601 people were injured or killed on the roads in Norway in 2022. This number is too high and highlights the necessity of putting road safety on the agenda. The car industry today is represented by a vast increase in advanced technology for drivers to interact with, and knowing how and when to use this technology is crucial for safe driving. Thus, our research question was: Does the Norwegian driver training industry teach advanced driver assistance technology (ADAS) and touch screens in their driver training?A survey was distributed to 1,058 driving schools in Norway, resulting in 333 responses. The results showed that driving instructors spend considerably more effort on teaching inbuilt driving assist technologies that enhance driving safety than on those that are designed to enhance safety but that, if not understood well, could lead to hazardous outcomes. Suggestions on how to improve driver training and driving instructor education are made. Research and practical implications are presented to make sure that driving education is updated and aligned with technological advancements in the automobile industry and to ensure traffic safety for all road users.

Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model

The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.

Cluster-based multicast optimized routing in VANETs using elite knowledge-based genetic algorithm

The advancement of autonomous and driver-assistance technology has greatly improved the experience of driving. With the growing use of vehicles, there is a greater need for effective communication among them to ensure both applications in Vehicular Ad-hoc Networks (VANETs) for safety and non-safety. VANETs commonly prioritize multicast communication to efficiently use computational resources and achieve a desirable Quality of Service (QoS). However, the dynamic nature of VANETs, marked by high mobility and frequent topology changes, presents significant challenges in establishing and sustaining multicast communication. To ensure that sensing data from all target points is reliably sent to the base station, it is crucial to address the important factors of energy balancing, load balancing, connectivity, and coverage. These factors play a vital role in achieving a balanced and efficient transmission system. In this research paper, clustering-based multicast routing is proposed that utilizes an enhanced Genetic Algorithm Elite Knowledge Sharing Genetic Algorithm (EKSGA) to choose cluster heads. The EKSGA identifies the top 5 % performers with the highest fitness score and generates a new population by exchanging genetic information, aiming to establish a population with the greatest potential for optimal fitness. The performance assessment of the suggested protocol demonstrates significant enhancements in network lifetime and throughput compared to various existing protocols, including LEACH, DEEC, p-WOA, DDEEE, ECBLTR, and GA with improvements ranging from 2.4 % to 22 % in network lifetime and from 3.2 % to 23.2 % in throughput for a system consisting of 500 nodes with energy level set at 0.1 J for rounds 300. Additionally, the proposed approach utilizes the existing population to generate a more fit population, thereby reducing the need for excessive iterations and computational overhead.

Digital Twin-Enabled Internet of Vehicles Applications

The digital twin (DT) paradigm represents a groundbreaking shift in the Internet of Vehicles (IoV) landscape, acting as an instantaneous digital replica of physical entities. This synthesis not only refines vehicular design but also substantially augments driver support systems and streamlines traffic governance. Diverging from the prevalent research which predominantly examines DT’s technical assimilation within IoV infrastructures, this review focuses on the specific deployments and goals of DT within the IoV sphere. Through an extensive review of scholarly works from the past 5 years, this paper provides a fresh and detailed perspective on the significance of DT in the realm of IoV. The applications are methodically categorized across four pivotal sectors: industrial manufacturing, driver assistance technology, intelligent transportation networks, and resource administration. This classification sheds light on DT’s diverse capabilities to confront and adapt to the intricate challenges in contemporary vehicular networks. The intent of this comprehensive overview is to catalyze innovation within IoV by providing an essential reference for researchers who aspire to swiftly grasp the complex dynamics of this evolving domain.

Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System.

Driver assistance systems can help drivers achieve better control of their vehicles while driving and reduce driver fatigue and errors. However, the current driver assistance devices have a complex structure and severely violate the privacy of drivers, hindering the development of driver assistance technology. To address these limitations, this article proposes an intelligent driver assistance monitoring system (IDAMS), which combines a Kresling origami structure-based triboelectric sensor (KOS-TS) and a convolutional neural network (CNN)-based data analysis. For different driving behaviors, the output signals of the KOS-TSs contain various features, such as a driver's pressing force, pressing time, and sensor triggering sequence. This study develops a multiscale CNN that employs different pooling methods to process KOS-TS data and analyze temporal information. The proposed IDAMS is verified by driver identification experiments, and the results show that the accuracy of the IDAMS in discriminating eight different users is improved from 96.25% to 99.38%. In addition, the results indicate that IDAMS can successfully monitor driving behaviors and can accurately distinguish between different driving behaviors. Finally, the proposed IDAMS has excellent hands-off detection (HOD), identification, and driving behavior monitoring capabilities and shows broad potential for application in the fields of safety warning, personalization, and human-computer interaction.

Eye Blink Detection- A New System for Driver Drowsiness and Distraction Detection

The advancement of technology has led to innovative solutions for ensuring road safety, particularly in the detection of driver drowsiness and distraction. This abstract presents a novel approach using eye blink detection as a fundamental component in a system designed to mitigate the risks associated with drowsy and distracted driving. The proposed system integrates cutting-edge computer vision techniques to accurately detect and analyze eye blinks in real-time. By utilizing high-resolution cameras installed within the vehicle, the system captures and processes images of the driver's face, focusing on the eyes to track blinking patterns and durations. Through sophisticated algorithms and machine learning models, it distinguishes between normal blinks and extended periods of eye closure, indicative of drowsiness or distraction. Furthermore, the system incorporates contextual awareness by considering various factors such as environmental conditions, driving behavior, and facial expressions to enhance the accuracy of detection. Machine learning algorithms trained on diverse datasets enable the system to adapt and improve its performance over time, making it more adept at recognizing subtle variations in blink patterns unique to each driver. The applications of this system are multifaceted. Firstly, it serves as an early warning system, alerting drivers when signs of drowsiness or distraction are detected. This proactive approach aims to prevent accidents by prompting drivers to take necessary breaks or refocus their attention on the road. Moreover, the system can integrate with existing driver assistance technologies, allowing for automated interventions, such as adjusting seat vibrations or emitting audible alerts to re-engage the driver. In conclusion, the proposed eye blink detection system represents a significant advancement in the realm of driver safety technologies. By leveraging sophisticated algorithms and real-time analysis of blink patterns, it offers a promising solution to mitigate the risks associated with driver drowsiness and distraction, ultimately contributing to safer roads and reducing the likelihood of accidents. KEYWORDS: Eye blinking, Detection, Driver, Drowsiness, Distraction, Detection, computer vision, machine learning, Deep learning, image processing.

Cognitive Aspect of Emotion Estimation of a Driver

Despite rapid advancements in the automotive industry, traffic safety risks persist. Addressing this challenge requires innovative driver assistance technologies. Common accidents result from driver inattention, fatigue, and stress, leading to issues like falling asleep at the wheel and improper acceleration and braking. Our study aims to contribute to advanced driver assistance systems that adapt to drivers' emotional needs, ultimately enhancing road safety. In this paper, we mention the result of our research to estimate drivers' emotions using sensors. For that purpose, we developed a sensor network containing sensors such as EEG, eye tracker, and driving simulator. We explored the relationship. As a result, we confirmed the relation between the driver's emotions, especially sleep conditions, driving speed, duration, and brain wave behavior.

6 Back-To-Drive Project. Assessment of Fitness-To-Drive in Older and Cognitively Impaired Adult Drivers: Adaptation of the on-Road Driving Observation Schedule to Simulation-Based Environments

Objective:Cognitive, motor and sensory deficits associated with aging, and with some neurological conditions such as acquired brain injury, may lead to severe driving performance impairment. While rehabilitation and driver assistance technologies may improve driving performance, the assessment of the actual fitness-to-drive of these people is challenging. Office-based neuropsychological/physical tests are considered insufficient to understand one’s ability to drive. The gold standard is the on-road assessment with dual control cars, superior in ecological validity, but expensive, stressful, and potentially unsafe. Valid, more cost-effective solutions for a safer, more accurate, standardized assessment of fitness-to-drive are currently needed. Modern and sensorized driving simulators offer key advantages, such as the possibility of exposing drivers to several relevant driving scenarios, including hazard situations, and of assessing their driving performance without being physically at risk. However, the extraction and direct interpretation of existing simulator-produced data may require specialized data processing skills or simulation expertise. To overcome this, we have developed an easy-to-use, pencil-and-paper observational instrument. The Sim-DOS is an adaptation of the widely used instrument to assess “natural driving”, the Driving Observation Schedule (DOS; Vlahodimitrakou et al., 2013).Participants and Methods:Via expert consensus, DOS targeted behaviors were adapted to a simulated-based environment (signaling, observation of environment, speed regulation, slow or unsafe reaction, distance interpretation, vehicle/lane positioning), and the Sim-DOS scores calculation (based on errors while doing such behaviors) was adapted from DOS to include hazard situations (HS, 0-100) and free driving (FD, 0-°°) scores. The instrument was then piloted with a sample of 35 older adults, along with the collection of simulator-produced data on number of harsh events and driving speed. Participants drove two consecutive 20-minutes long scenarios, with low and high traffic density (LTD, HTD). In each scenario, there were periods with and without potentially hazard situations.Results:Assessments were performed by two independent trained observers, producing substantial inter-rater reliability (intra-class correlation coefficients above 0.94). Participants (70.7±4.1 years old, 60% male, 46.1±6.7 years of driving experience) were mostly regular drivers (74%). However, psychomotor skills of the majority were compromised, with only one participant being above the 80th percentile in the reaction times test of the national mandatory driving assessment. When exposed to hazard situations, most of the participants (94.1%) did not perform well, independently of traffic density, with average Sim-DOS-HS scores of 87.1±9.7 (out of 100, t-values>7.3, p-values<.05).Compared to LTD scenarios, in HTD scenarios participants drove less smoothly (HTD:0.97±1.24 vs. LTD:0.33±0.58 of harsh events, Z=3.1, p<.05). However, they also drove slower (HTD:82.41±27.43 vs. LTD:103.55±14.61 km/h, t=5.2, p<.05), improving their ability to manage hazard situations, and therefore producing higher than expected Sim-DOS scores (HTD:87.05±10.28). During free driving, participants performed worse under LTD conditions (Sim-DOS-FD scores: HTD:11.68±6.20 vs. LTD:14.40±9.58, t=2.15, p<.05) as they drove at higher speed (HTD:85.01±24.28 vs. LTD:104.70±11.94 km/h, t=5.8, p<.05), although they did it more smoothly (HTD:1.94±3.74 vs. LTD:0.45±0.74 harsh events, Z=2.65, p<.05).Conclusions:Our study provides a validated driving assessment tool for use in driving simulators that will allow a safer, more ecologic, holistic and informative evaluation of the fitness-to-drive of older adults and neurological patients.

Driving Difficulties and Preferences of Advanced Driver Assistance Systems by Older Drivers With Central Vision Loss.

The purpose of this study was to investigate driving difficulties and Advanced Driver Assistance Systems (ADAS) use and preferences of drivers with and without central vision loss (CVL). Fifty-eight drivers with CVL (71 ± 13 years) and 68 without (72 ± 8 years) completed a telephone questionnaire. They rated their perceived driving difficulty and usefulness of technology support in 15 driving situations under good (daytime) and reduced visibility conditions, and reported their use experience and preferences for 12 available ADAS technologies. Drivers with CVL reported more difficulty (P = 0.002) and greater usefulness of technology support (P = 0.003) than non-CVL drivers, especially in reduced visibility conditions. Increased driving difficulty was associated with higher perceived technology usefulness (r = 0.34, P < 0.001). Dealing with blind spot road users, glare, unexpected pedestrians, and unfamiliar areas were perceived as the most difficult tasks that would benefit from technology support. Drivers with CVL used more advanced ADAS features than non-CVL drivers (P = 0.02), preferred to own the blind spot warning, pedestrian warning, and forward collision avoidance systems, and favored ADAS support that provided both information and active intervention. The perceived benefits of and willingness to own ADAS technologies were high for both groups. Drivers with CVL used more advanced ADAS and perceived greater usefulness of driver assistance technology in supporting difficult driving situations, with a strong preference for collision prevention support. This study highlights the specific technology needs and preferences of older drivers with CVL, which can inform future ADAS development, evaluation, and training tailored to this group.

Sharing Vehicle Situation Awareness Reduces Driver-Initiated Overrides in Urban Environments

Driver assistance technologies have rapidly advanced. However, using partially automated driving systems in urban environments is still challenging. The potential disuse of driving automation is one of the challenges that prevents users from taking full advantage of the system. To address this issue, we investigated whether sharing the vehicle’s situation awareness (SA) information could increase the proper use of driving automation in urban contexts. An Augmented Reality Head-Up Display (AR HUD) was developed to present the vehicle’s SA information, and its effect was tested using a driving simulator. We used a two-part mixed model to analyze driver reliance behavior. The results showed that sharing the vehicle’s SA information decreased override responses when the automation could handle the situation but had no significant effect on the override time. These findings suggest that providing drivers with the vehicle SA information can increase the appropriate use of driving automation in complex urban driving situations.

Auditory neural correlates and neuroergonomics of driving assistance in a simulated virtual environment

Objective. Driver assistance systems play an increasingly important role in modern vehicles. In the current level of technology, the driver must continuously supervise the driving and intervene whenever necessary when using driving assistance systems. The driver’s attentiveness plays an important role in this human–machine interaction. Our aim was to design a simplistic technical framework for studying neural correlates of driving situations in a functional magnetic resonance imaging (fMRI) setting. In this work we assessed the feasibility of our proposed platform. Methods. We proposed a virtual environment (VE) simulation of driver assistance as a framework to investigate brain states related to partially automated driving. We focused on the processing of auditory signals during different driving scenarios as they have been shown to be advantageous as warning stimuli in driving situations. This provided the necessary groundwork to study brain auditory attentional networks under varying environmental demands in an fMRI setting. To this end, we conducted a study with 20 healthy participants to assess the feasibility of the VE simulation. Results. We demonstrated that the proposed VE can elicit driving related brain activation patterns. Relevant driving events evoked, in particular, responses in the bilateral auditory, sensory-motor, visual and insular cortices, which are related to perceptual and behavioral processes during driving assistance. Conceivably, attentional mechanisms increased somatosensory integration and reduced interoception, which are relevant for requesting interactions during partially automated driving. Significance. In modern vehicles, driver assistance technologies are playing an increasingly prevalent role. It is important to study the interaction between these systems and drivers’ attentional responses to aid in future optimizations of the assistance systems. The proposed VE provides a foundational first step in this endeavor. Such simulated VEs provide a safe setting for experimentation with driving behaviors in a semi-naturalistic environment.

Adaptive forward collision warning system for hazmat truck drivers: Considering differential driving behavior and risk levels

The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver’s response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems.

Design and simulation of automotive radar for autonomous vehicles

Modern automobile technology is pushing towards maximizing road safety, connected vehicles, autonomous vehicles, etc. Automotive RADAR is core sensor technology used for ADAS (Advanced Driver Assistance Technology), ACC (Adaptive Cruise Control), AEB (Automatic Emergency Braking System), traffic assistance, parking aid, and obstacle/pedestrian detection. Despite being inexpensive, RADAR technology provides robust results in harsh conditions such as harsh weather, extreme temperature, darkness, etc. However, the performance of these systems depends on the position of the RADAR and its characteristics like frequency, beamwidth, and bandwidths. Moreover, the characterization of varied materials like layers of paint, polish, primer, or layer of rainwater needs to be analyzed. This performance can be predicted through real-time simulation using advanced FEM software like Altair FEKO&amp;WinProp. These simulations can provide valuable insight into the performance of the system, allowing engineers to optimize the system for specific use cases. For example, simulation can be used to determine the optimal parameters of the RADAR system for a given application. This information can then be used to design and build a physical model or prototype that is optimized for the desired performance. These simulations play a prominent role in determining appropriate data collection and sensor fusion, which reduces the cost and time required for the development of a physical model or prototype. The continued growth and demand for advanced safety features in vehicles further highlight the importance of RADAR technology in modern automobile technology. By accurately characterizing the environment and simulating the system's behavior in real time, engineers can optimize RADAR systems for specific use cases, contributing to safer and more efficient driving experiences

Simultaneous Estimation of Vehicle Sideslip and Roll Angles Using an Integral-Based Event-Triggered H$_{\infty }$ Observer Considering Intravehicle Communications

In recent years, several technological advances have been incorporated into vehicles to ensure their safety and ride comfort. Most of these driver-assistance technologies aim to prevent skidding, whereas less attention has been paid to the avoidance of other dangerous situations such as a rollover. Since knowledge of slip and roll angles is critical to the control and safety of vehicle handling, their estimation remains of great interest when addressing emerging constraints in modern technologies involving networked communications and distributed computing. This paper presents an integral-based event-triggered H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{\infty }$</tex-math></inline-formula> observer to simultaneously estimate the sideslip and roll angles, considering intravehicle communications with a networked-induced delay. As the longitudinal velocity and tire cornering stiffness of a vehicle can vary significantly during driving and have a strong influence on vehicle lateral stability, these time-varying parameter uncertainties are considered in the design of the observer. The simulation and experimental results demonstrate the effectiveness of the proposed observer.

How Older Drivers Perceive Warning Alerts? Insights for the Design of Driver-Car Interaction.

The automotive industry is working toward driving automation and driver-assistance technology is becoming a norm in modern cars. Warning alert systems support the driver-car interaction and inform drivers about automation system status, upcoming obstacles, or dangers ahead. However, older drivers' needs are not always addressed in research studies, although they make up a large segment of drivers. Therefore, we conducted a qualitative three-round formative evaluation of a warning alert system using video prototypes in lab and remote settings. The goal was to evaluate visual-, sound-, and speech-based alerts based on: (a) their efficiency in informing drivers about the road situation ahead, and (b) participants' subjective opinions. We evaluated the system's efficiency using self-reported data measuring participants' cognitive load, usability, UX, and ease of use. Also, we conducted interviews to collect subjective feedback about proposed prototypes. In this article, we describe the design of warning alerts and report on their evaluation results. Our results show that speech-based warnings, especially when coupled with visual warnings, are efficient and accepted well by the participants. This article illustrates older drivers' attitude toward the use of different warning modalities in the driving context.

Advanced Driver Assistance Systems in Top-Selling Vehicles in the United States: Cost, Vehicle Type, and Trim Level Disparities

How are driver assistance technologies available and priced across different vehicle models and trim levels? This study examines the availability of blind spot detection, automatic emergency braking, and forward collision warning on the top-selling passenger vehicles in the United States. We found that these technologies are not uniformly available and safety features are less likely to occur on base models or less-expensive trim packages, particularly of larger vehicles and among US versus international automakers. This represents a cause for concern due to the greater dangers posed by SUVs and light duty trucks and their popularity in the United States.