The use of smartphones and in-vehicle infotainment systems has made distracted driving a major cause of traffic accidents. Image-based models are essential for advanced driver assistance systems (ADAS) to detect distractions and alert drivers. However, a major challenge is the poor generalization of image-based detection models, particularly in cross-domain scenarios. Given the variability of real-world driving environments, it is essential to develop a model that performs consistently and efficiently across diverse conditions. In real world, due to the differences between the training scenarios (i.e., source domain) and the application scenarios (i.e., target domain), relying solely on either local alignment or global alignment cannot effectively address the various domain shift scenarios. Therefore, we propose DAF (Dynamic Adaptation Framework), a novel domain adaptation approach that automatically handles cross-domain shifts through dynamic feature alignment. Unlike conventional methods, DAF jointly optimizes global context preservation and local feature discrimination in a unified framework, continuously adapting to domain discrepancies through integrated representation learning. Experiments are conducted on three cross-domain driver distraction datasets including State-farm, AUC-Real, and AUC-Laboratory. The results demonstrate that the proposed method outperforms the compared methods, offering a potential choice for enhancing distraction detection performance in diverse scenarios.

This study assesses a Vehicle-to-Pedestrian (V2P) collision warning system compared to conventional vehicle-issued auditory alerts in a real-world scenario simulating a vehicle on a fixed track, characterized by limited maneuverability and the need for timely pedestrian response. The results from analyzing speed variations show that V2P warnings are particularly effective for pedestrians distracted by phone use (gaming or listening to music), highlighting the limitations of auditory alerts in noisy environments. The findings suggest that V2P technology offers a promising approach to improving pedestrian safety in urban areas

The majority of research on safety in autonomous vehicles has been conducted in structured and controlled environments. However, there is a scarcity of research on safety in unregulated pedestrian areas, especially when interacting with public transport vehicles like trams. This study investigates pedestrian responses to an alert system in this context by replicating this real-world scenario in an environment using an autonomous vehicle. The results show that safety measures from other contexts can be adapted to shared spaces with trams, where fixed tracks heighten risks in unregulated crossings.

Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm.

The problem of calibration from straight lines is fundamental in geometric computer vision, with well-established theoretical foundations. However, its practical applicability remains limited, particularly in real-world outdoor scenarios. These environments pose significant challenges due to diverse and cluttered scenes, interrupted reprojections of straight 3D lines, and varying lighting conditions, making the task notoriously difficult. Furthermore, the field lacks a dedicated dataset encouraging the development of respective detection algorithms. In this study, we present a small dataset named “ClearLines”, and by detailing its creation process, provide practical insights that can serve as a guide for developing and refining straight 3D line detection algorithms.

Designing brain-computer interface systems for electroencephalogram (EEG)-based driver drowsiness recognition remains a significant challenge due to the significant variation in EEG signals across subjects and recording sessions. To address this problem, this paper develops a novel two-stage information transfer strategy framework for domain generalization. The framework has two domain mappers to reduce the distribution differences of EEG features from different individuals, a mapper mix block for generating hybrid mapping features, and a domain adversarial neural network (DANN) for drowsiness recognition based on hybrid EEG features. In the process of DANN to capture common features, we additionally employ two models based on self-attention mechanism to capture domain-invariant attention relationships between electrode channels and between frequency bands. Experimental results show that the proposed framework achieves an average accuracy of 81.34% in the leave-one-out cross validation for driver drowsiness recognition, which is higher than the state-of-the-art model with the number of 79.37%. In addition, we explore the impact of EEG features from different frequency bands and brain regions on this cross-subject task. The results show that EEG features from delta, theta and alpha bands can achieve much better performance than the other two bands, and features from the frontal lobe region perform better than the other regions. These findings reveal domain-invariant features and their relationships with brain regions and frequency bands, enhancing our understanding of the underlying messages of EEG signals.

Teams dedicated to research in the field of autonomous vehicles can be found in many universities and research centers, but they often face the challenge of finding a suitable platform for their work. The primary reason for this challenge is the inaccessibility and high cost of commercial autonomous vehicles, leading researchers to rely on simulators.This paper introduces a new software architecture designed to automate vehicles, providing all the necessary capabilities of an autonomous vehicle in a more cost-effective and efficient manner. The architecture is designed to be modular, universal, and with a public interface, making it easy to modify, adapt to any type of vehicle, and accessible to any researcher.The new software architecture has been implemented in two platforms: a vehicle integrated with OpenPilot via ROS2 without any external hardware, and a last-mile robot. A validation test was conducted with volunteers to assess the reaction of passengers while the car was driving autonomously. The results of this implementation demonstrate the potential of this new software architecture to provide a comprehensive and accessible platform for the advancement of autonomous vehicle research.

The continuous advance of the automotive industry is leading to the emergence of more advanced driver assistance systems that enable the automation of certain tasks and that are undoubtedly aimed at achieving vehicles in which the driving task can be completely delegated. All these advances will bring changes in the paradigm of the automotive market, as is the case of insurance. For this reason, CESVIMAP and the Universidad Carlos III de Madrid are working on an Autonomous Testing pLatform for insurAnce reSearch (ATLAS) to study this technology and obtain first-hand knowledge about the responsibilities of each of the agents involved in the development of the vehicles of the future. This work gathers part of the advancements made in ATLAS, which have made it possible to have an autonomous vehicle with which to perform tests in real environments and demonstrations bringing the vehicle closer to future users. As a result of this work, and in collaboration with the Johannes Kepler University Linz, the impact, degree of acceptance and confidence of users in autonomous vehicles has been studied once they have taken a trip on board a fully autonomous vehicle such as ATLAS. This study has found that, while most users would be willing to use an autonomous vehicle, the same users are concerned about the use of this type of technology. Thus, understanding the reasons for this concern can help define the future of autonomous cars.

With the race towards higher levels of automation in vehicles, it is imperative to guarantee the safety of all involved traffic participants. Yet, while high-risk traffic situations between two vehicles are well understood, traffic situations involving more vehicles lack the tools to be properly analyzed. This paper proposes a method to compare Surrogate Safety Measures values in highway multi-vehicle traffic situations such as lane-changes that involve three vehicles. This method allows for a comprehensive statistical analysis and highlights how the safety distance between vehicles is shifted in favor of the traffic conflict between the leading vehicle and the lane-changing vehicle.

In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee on Transportation 5.0 with the mission of promoting and transforming the deployment of advanced and innovative technologies, especially Artificial Intelligence in transportation. This paper briefly summarizes our main research and findings over the last decade. Transportation Foundation Models, Transportation Scenarios Engineering, and Transportation Operating Systems have been identified as the main directions for the research and development of next-generation intelligent transportation systems.

The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle’s performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.

In conditional automation, a response from the driver is expected when a take over request is issued due to unexpected events, emergencies, or reaching the operational design domain boundaries. Cooperation between the automated driving system and the driver can help to guarantee a safe and pleasant transfer if the driver is guided through a haptic guidance system that applies a slight counter-steering force to the steering wheel. We examine in this work the impact of haptic guidance systems on driving performance after a take over request was triggered to avoid sudden obstacles on the road. We studied different driver conditions that involved Non-Driving Related Tasks (NRDT). Results showed that haptic guidance systems increased road safety by reducing the lateral error, the distance and reaction time to a sudden obstacle and the number of collisions.

In navigation activities, kinematic parameters of a mobile vehicle play a significant role. Odometry is most commonly used for dead reckoning. However, the unrestricted accumulation of errors is a disadvantage using this method. As a result, it is necessary to calibrate odometry parameters to minimize the error accumulation. This paper presents a pipeline based on sequential least square programming to minimize the relative position displacement of an arbitrary landmark in consecutive time steps of a kinematic vehicle model by calibrating the parameters of applied model. Results showed that the developed pipeline produced accurate results with small datasets.

Communication between vehicles with varying degrees of automation is increasingly challenging as highly automated vehicles are unable to interpret the non-verbal signs of other road users. The lack of understanding on roads leads to lower trust in automated vehicles and impairs traffic safety. To address these problems, we propose the Road User Communication Service, a software as a service platform, which provides information exchange and cloud computing services for vehicles with varying degrees of automation. To inspect the operability of the proposed solution, field tests were carried out on a test track, where the autonomous JKU-ITS research vehicle requested the state of a driver in a manually-controlled vehicle through the implemented service. The test results validated the approach showing its feasibility to be used as a communication platform. A link to the source code is available.

Connected and Automated Vehicles (CAVs) have received a lot of attention in recent years. However, there are still numerous challenges in this field. In this paper, we investigated the effects of dynamic-flexible platooning on travel time by considering real-world trips data. For this purpose we extended the platooning capabilities of the 3DCoAutosim simulation platform, and proposed a dynamic-flexible model that we validated by creating use cases on traffic efficiency. We studied our dynamic-flexible platooning case for three electric vans with an autonomous leader, a semi-autonomous first follower with a driver, and an autonomous last follower. Results showed that the model developed in this study is efficient to investigate the effects of dynamic-flexible platooning on travel time.

Several approaches have been presented during the last decades to reduce carbon pollution from transportation. One example is the use of platooning mode. This paper considers data obtained from daily trips to investigate the impact of platooning on travel time, emissions of CO2, CO, NOxand HC and fuel consumption on a road network in Upper Austria. For this purpose, we studied fuel combustion-based engines relying on the extension of the 3DCoAutoSim simulation platform. The obtained results showed that the platooning mode not only increased driving efficiency but also decreased the total emissions by reducing fuel consumption.

In the context of Intelligent Transportation Systems and the delivery of goods, new technology approaches need to be developed in order to cope with certain challenges that last mile delivery entails, such as navigation in an urban environment. Autonomous delivery robots can help overcome these challenges. We propose a method for performing mixed reality (MR) simulation with ROS-based robots using Unity, which synchronizes the real and virtual environment, and simultaneously uses the sensor information of the real robots to locate themselves and project them into the virtual environment, so that they can use their virtual doppelganger to perceive the virtual world. Using this method, real and virtual robots can perceive each other and the environment in which the other party is located, thereby enabling the exchange of information between virtual and real objects. Through this approach a more realistic and reliable simulation can be obtained. Results of the demonstrated use-cases verified the feasibility and efficiency as well as the stability of implementing MR using Unity for Robot Operating System (ROS)-based robots.

Rapid advancements in driver assistance technology will lead to the integration of fully autonomous vehicles on our roads that will interact with other road users. To address the problem that driverless vehicles make interaction through eye contact impossible, we describe a framework for estimating the crossing intentions of pedestrians in order to reduce the uncertainty that the lack of eye contact between road users creates. The framework was deployed in a real vehicle and tested with three experimental cases that showed a variety of communication messages to pedestrians in a shared space scenario. Results from the performed field tests showed the feasibility of the presented approach.

This paper introduces a method to represent Takagi-Sugeno Fuzzy Control Systems (FCSs) as computational graphs, so they can be adjusted through a supervised training process based on gradient descent. It has been tested both with artificial (i.e. a known fuzzy controller) and naturalistic (i.e. driver's data extracted from the vehicle and the environment) data. The results achieved show high conformance to synthetic data, and seem to describe a car-following behavior with quite good precision, which suggests that it is possible to model the driver's behavior in a longitudinal model based on if-then type rules.

The protection of vulnerable road users (VRUs) has been an active research topic in recent years. In this context, P2V (Pedestrian-to-Vehicle) and V2P (Vehicle-to-Pedestrian) communication systems have become crucial technologies to minimize potential dangers, due to the high detection rates and the high user-satisfaction levels they achieve. The recent release of the latest versions of SUMO (Simulation of Urban Mobility) and TraaS (TraCI as a Service) reveals the possibility of developing both P2V and V2P communications in a simulated 3D environment with microscopic modeling of vehicles and pedestrians, as well as the possibility of testing the efficiency of the proposed system. To this end, we implemented a 3D driving simulator with pedestrian integration, using a powerful game engine (Unity 3D) that accessed data from the SUMO open source traffic simulator and was therefore capable of simulating vehicle-pedestrian interaction through Transmission Control Protocol (TCP) connection. Results from a defined use case validated the presented approach.

Trust plays a decisive role in the public's acceptance of the new self-driving car technology. In order to better understand how to promote confidence in vehicle automation safety among the public, we studied pedestrian behavior shortly before and while crossing a marked crosswalk. Such information is also essential for setting parameters for automated vehicles to act accordingly during interactions with pedestrians. Through the analysis of the recorded videos and subjective qualitative data, we identified factors that potentially influence the perception of a road situation as safe in an environment in which vehicles operate with full driving automation (level 5) in a public space. A variety of responses were observed that exhibit several levels of trust, uncertainty and a certain degree of fear. It became clear, however, that the longer the people interacted with the vehicles, the more confident and trusting they became in automation capabilities. The existence of a communication system to interact with driverless vehicles was also evaluated as positive.

Disregard for the rules regarding the minimum safety distance can make the avoidance of a rear-end collision nearly impossible. In a joint effort to enhance safety and improve the decision making processes on an individual level, we contribute to the state of the art with an innovative and affordable system that identifies vehicles and provides a rear-end distance warning system capable of recognizing dangerous situations, and which can also inform other vehicles of the danger, independent of their communication capabilities or equipment. Vision sensors garner information through the stereoscopic capturing and processing of images by rear cameras to calculate the distance between the leading and following vehicles. Visual data related to the safety distance is provided to the following vehicle in real-time, relying on an asynchronous collaborative process. A detailed error analysis of the distance calculation is provided based on the measurement procedure and roadway geometry. Relying on the communication between the two vehicles, an in-vehicle system was compared to the rear-mounted distance warning system under lab-controlled conditions. Both human–machine interaction paradigms were evaluated in terms of their impact on driver response. Results showed that both systems influenced the driver in keeping a time gap of two seconds.

The work presented on this paper, aims to provide an automated tool to analyze the autonomous vehicle-pedestrian interaction. It detects pedestrians in the environment, by means of the most modern visual detection technology available in the literature, applied to an stereo camera system, and analyses if the driver looks at the vehicle. To achieve this task the algorithm does pose estimation, feature matching, and facial detection to acquire the position of the pedestrians and distinguish if they notice the vehicle. The algorithm calculates the 3D coordinates of a given pedestrian using the vehicle as a reference, tracking the movement of the pedestrian during all the process, thus providing meta-information of this interaction, which allows to process this information at higher levels: e.g. if the pedestrians feels conformable to cross, or if he performs any other maneuver moving away from the trajectory. The proposed algorithm was tested in campus scenarios where pedestrians and vehicle shared the environment, and the results proved the viability, providing a tool, useful for researchers which allows to process high amounts of information without the need of supervision.

Human drivers in autonomous vehicles will monitor the system and be ready to resume control in ambiguous or emergency situations. As a driver's reaction time to intervene after having realized a problem has occurred can be critical, we present the Interactive Automation Control System (IACS) to assist the driver when their takeover is required. The system displays manual or automated mode in an unobstrusive location in the vehicle, signaling when a TOR is necessary. We evaluate the system's performance during a situation in which the automation has not been defined to operate and study its impact on the overall driving performance, specifically the driver's reaction time to a Take Over Request (TOR). Results showed significant improvements in driving performance with the proposed system. Both the response time to the TOR and the number of collisions decreased when the IACS was activated. Subjective ratings of the system regarding its performance showed high satisfaction levels.

The recent technological advances on telecommunications create a new reality on mobility sensing. Nowadays, we live in an era where ubiquitous digital devices are able to broadcast rich information about human mobility in real-time and at a high rate. Such fact exponentially increased the availability of large-scale mobility data which has been popularized in the media as the new currency, fueling the future vision of our smart cities that will transform our lives. The reality is that we just began to recognize significant research challenges across a spectrum of topics. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders on building knowledge discovery pipelines over such data sources. However, such availability also raises privacy issues that must be considered by both industrial and academic stakeholders on using these resources.

Driver State Monitoring Systems (DSMS) play a crucial role in determining whether the driver is prepared to take control of the vehicle or not. In this paper we present a cost-efficient, context-aware system, based on smartphones, which supports the driver in the driving task. This is through monitoring the driver attention and drowsiness, in addition to detecting pedestrians and vehicles. The detection rates obtained 85% in the case of pedestrians and over 90% in the case of vehicles, along with the results of the driver state, measured by closed eye duration, proved the viability of the proposed approach to provide situational awareness in the novel paradigm of autonomous driving.

Vehicular systems that intend to aid the driver to increase road safety and reduce road accidents might differ in the levels of safety provided if they are based on different functioning and visualization concepts. In this paper we present a flexible and adjustable 3-dimensional driving simulator based on Open-StreetMap (OSM) data that integrates Vehicular Ad Hoc Network (VANET) communication capabilities to assess different information paradigms in a lab-controlled environment. Results regarding the proof of concept of the simulator to test new in-vehicle technology validated the proposed approach. The experiments conducted show discrepancies in the driver's environment perception that were determined by the system used and how the information was provided to the driver.

Assessment of the driver's state and the driving environment is essential in promoting road safety in both manual and automatic driving paradigms where the monitoring tasks are either performed by the driver or by the system. Within this work, we present a cost effective mobile application to measure gaze behavior and analyze road conditions for a request to take vehicle's control in case of an automatic driving or to avoid inattentive driving in a manual driving paradigm. We evaluated the application under daylight conditions. Results showed a high rate of detections in a short period of time.

Ground detection algorithms are important for locating the area where a vehicle can travel during autonomous navigation or for collision avoidance Advanced Driver Assistance Systems (ADAS). Although many algorithms rely on a single camera to detect the ground, they don't consider the possibility of combining transformation approaches with additional information that could be obtained from another camera mounted on the vehicle. In this paper, we propose an algorithm for ground detection that allows the use of disparity maps and the combination of infrared information to enhance road detection. To this end we propose an original approach based on a thermal stereo system to analyze the thermal features of the road that are then complemented by the depth information obtained from the disparity map. We tested the algorithm with a variety of complex scenarios and results showed that the 2D and 3D data enabled accurate detection of the road independent of the weather conditions.

The development of Advanced Driver Assistance Systems (ADAS) is rapidly growing. However, most of the ADAS require field test, which is expensive, unpredictable and time consuming. In this paper we propose a multiagent-based driving simulator which integrates a human factor analysis suite and enables rapid and low-cost experimentation of mobile-device ADAS. Our architecture uses a microscopic simulator and a serious-game-based driving simulator. The latter allows the user to control a vehicle and change the correspondent simulation state in the microscopic simulator. The driving simulator also connects to an Android device and sends several kinds of data, such as current GPS coordinates or transportation network data. One important feature of this architecture is its suitability to serve as an appropriate means to conduct behaviour elicitation through peer-designed agents, so as to improve modelling of various driving styles accounting for different aspects of preferences and perception abilities, as well as other performance measures related to drivers' interaction with ADAS solutions. The potentials of our approach to aid experiments in human factor analysis are still to be tested, but are undoubtedly huge and encouraging.

Traveling with children in tow can pose a serious distraction to the driver, effectively drawing much of the necessary attention away from the road and causing a disruption in normal driving patterns. In this paper we investigate the driver's capacity to operate a vehicle safely when being exposed to a noise stimulus, specifically in the form of a crying baby for an extended period of time. For this purpose, we developed a tailored driving simulator framework to efficiently configure new experiments, built on modular components to make it easier to upgrade and update the experiment scenario and overall conditions. We then compared the driving behavior of parents to individuals without children focusing particularly on the affects on driving performance when a sudden event occurred on the road. We aim to study driving patterns under stressful conditions such as having children as occupants in the vehicle to be able to classify drivers for background training purposes regarding in-vehicle behavior. Results have shown the tendencies of parents when having a baby in the vehicle to produce better driving performances.

Advanced driver assistance systems (ADAS) and driver information systems (DIS) do not always comply with the intended driver safety enhancement. Even if they aim to augment the driver's awareness of the surrounding environment, perceiving this information requires the occasional attention diversion from the road, which could lead to a loss of vehicle control if the total eyes-off-road time exceeds the National Highway Traffic Safety Administration (NHTSA) recommendation for glances away from the roadway. Additionally, technologies that can be found in other mobile environments, smartphones, and tablets are increasingly being integrated into cars, providing a necessary facet of study and continued research in their effects. We addressed this question by analyzing differential preferences for the layout of DIS and ADAS compared with existing ones through a card-sorting experiment. To validate our data, we additionally studied the drivers' performance and gaze with the preferred locations for in-vehicle information through gaze location and speed metrics measurements. Our validation process showed that the time the drivers needed to find the conveyed information in the preferred layout was within the recommended time of the NHTSA Guidelines. Drivers' preferences with regard to the functional layout of current DIS and ADAS compared with existing ones did not essentially differ from the layouts that are currently on the market. However, including mobile applications and social media in a vehicular context was not considered necessary.

The thirteen papers in this special section present issues related to the analysis of human factors in the design and evaluation of IV technologies, in a wide spectrum of applications and in different dimensions. It builds upon a proper environment to disseminate knowledge and motivate interactions among the technical and scientific communities, practitioners, and students, allowing for state-of-the-art concepts to be devised, tested, and evaluated.

Currently, the processes used by many car manufacturers to adapt information from the head unit to the intended display platform are outdated and extremely cumbersome. The individual graphic elements are not designed for use in different contexts and many steps must be done manually to achieve a proper in-vehicle information visualization. Additionally, the amount of data that must be maintained is extremely large, resulting in strong restrictions in the variability of appearance and displayed information. An additional challenge is that multiple car brands belong to the same main company with each brand having a separate identity. Therefore, the graphical user interface (GUI) elements require resizing and recomposition which then reflects the brand's heterogeneous characteristics. To overcome these drawbacks we present a software solution to create and edit flexible, in-vehicle GUIs through reusable elements or widgets that adjust their size and composition to their environmental context in a dynamic and automatic manner. We have examined the quality of the tool through validation rules for each step and proposed calculation algorithms as a possible approach for a largely automated evaluation.

The incorporation of Augmented Reality (AR) in the windshield of automobiles using heads-up displays (HUD) is starting to be implemented by some manufacturers and is proving to be very useful in some situations, including safety distance keeping. This paper reports on a system that warns the driver via the car's HUD when he violates the predefined safety distance, to avoid a possible forward collision, and proposes two different visualization metaphors based on traffic signals. The metaphors are compared, with and without warning sounds, through computer simulations performed by 22 participants from different age groups and driving experiences. One of the metaphors corresponds to a variant of the traffic sign C10 that forbids circulating from the preceding vehicle shorter than a certain minimum headway, whereas the other corresponds to the road safety marks, which recommend the safety distance to be observed from the vehicle ahead. Results show that the metaphor derived from the safety marks, with warning sounds, is preferred by the participants, and was considered the most useful, intuitive and adequate to a forward collision warning. We also found that this metaphor was preferred by all participants that were 42 or more years old, whereas participants between 28 and 41 years old were divided between the two metaphors, with warning sounds.

Tough competition in the automobile market makes it very important to exceed customer expectations with in-vehicle devices or applications in order to stand out in an already saturated market. Research on human-machine interaction (HMI) showed that user satisfaction is not only influenced by usability factors but also by user experience (UX). Since UX in the automotive context has not yet been comprehensively investigated, this article presents a feasible research method for the measurement of UX. We rely on the fulfillment of psychological needs as a user experience measurement and conducted three online surveys to develop a questionnaire for that purpose. We analyze nine need scales, each with 10 items, and reduced the item count afterwards for a shorter form of the questionnaire. Results reveal that the method successfully measured the intended needs. Future work with respect to a further extension and improvement of the method is discussed.

Systems developed to be operated in a vehicular environment have gradually begun to include further applications, which can be found in other mobile environments, such as smart phones and tablets. This continued growth could overwhelm the driver and affect road safety. Thus, it is crucial to ensure that these devices provide the type of information drivers need. This paper focuses on the presentation of in-vehicle information while driving. Function clusters and information prioritization for different modules in Driver Information Systems are primarily investigated through driver preferences analysis. Results are evaluated outlining implications for proper location of information on the in-vehicle displays.

The advent of infrastructureless vehicle-to-vehicle (V2V) communication has opened the opportunity to design driver-assistance systems that collect information from sensors residing in neighboring vehicles. Windshield-installed cameras are one example of a sensor that is becoming common in modern vehicles. Remotely accessing real-time images of these cameras using V2V communication enables a significant increase in the visual awareness of each driver. In this paper, we propose and evaluate the performance of a driver-assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. This cooperative system is able to increase the visibility of drivers intending to overtake, thus making such critical maneuvers safer. Our system uses an augmented reality human–machine interface that is able to convey the increased visibility perspective in a straightforward fashion. We also show that the required latency for this intervehicle communication can be obtained using the Dedicated Short-Range Communications (DSRC) proposed for vehicular environments.

A detailed study of cultural differences can facilitate the process of introducing a product into a particular market. Such an analysis can be used to decide to what extend a global design of a product needs to be considered and which subsequent measures related to localization or adaptation to a specific target culture need to be taken. In the particular case of a Driver Information System (DIS), cultural preferences in the interaction with the user interface could lead to a decrease of the ease of navigation through an insufficiently localized human machine interface depending on the culture. In this study we examine cultural differences on the interaction with the menu of a DIS through a cross cultural survey. The web based comparative multilingual questionnaire was analyzed to determine the impact of the user's culture on preferences in the design of the menu structure of a system to be used in a vehicular environment. Results confirm the existence of cultural differences among users from Germany, USA and Japan. In particular we show heterogeneity that affects the menu structure design of the DIS' user interface. The implications for the design and adaptation of entertainment related functions in the DIS' menu structure are outlined.

The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.

Voice-operated devices are of particular interest in mobile environments, e.g. vehicles. They promise a natural and intuitive interface to devices and services, and they offer hands-free operation, a legal prerequisite for in-car usage in many European countries. Spelling is a common task for the operation of voice operated devices, especially under unfavorable communication conditions. This paper presents a first analysis of the error and fluency rate for 4502 utterances from the German SpeechDat-Car database. The error rate was found to be between 1.7 natural items, and between 3.6 letter sequences. Only 3.6 hesitations. These results suggest that spelling while driving might be a suitable means of fallback interaction if specific error recovery mechanisms are implemented