Search

The accident mortality rates are rapidly increasing due to driver inattention, and traffic accidents become a significant problem on a global scale. For this reason, advanced driver assistance systems (ADASs) are essential to enhance traffic safety measures. However, adverse environmental factors, weather, and light radiation affect the sensors' accuracy. Furthermore, potential risks may go unreported if they obstruct the sensor's line of sight or are outside its limited field of view. To overcome these problems, this research presents a vehicle collision estimate warning system that leverages a combined approach of a local linear wavelet neural network (LLWNN) and an unscented Kalman filter (UKF). The system integrates sensor data and vehicle-to-everything (V2X) communication to enhance the accuracy and reliability of vehicle state estimation and collision prediction. The LLWNN module is responsible for forecasting the future states of the vehicle based on historical sensor measurements. This powerful time-series modeling technique allows the system to anticipate the vehicle's trajectory and potential collision risks. The UKF then optimally fuses the LLWNN predictions with the real-time sensor data, including information received through V2X communication, to generate accurate, up-to-date estimates of the vehicle's state. The V2X technology enables the seamless exchange of critical safety information between the host vehicle, surrounding vehicles, infrastructure, and other road users. This includes data on vehicle position, speed, acceleration, and intended maneuvers. By incorporating this V2X-based situational awareness, the system can better perceive the dynamic traffic environment and identify potential collision threats that may be outside the line of sight or detection range of the vehicle's onboard sensors alone. The LLWNN-based UKF module then processes this rich, multimodal data to provide timely and pertinent collision alerts to the driver. These alerts can warn the driver of impending collisions with distant objects, enabling them to take appropriate evasive action. By implementing this integrated LLWNN-UKF approach leveraging sensor data and V2X communication, we aim to reduce the number of collisions caused by reckless driving, which will lead to a decrease in traffic-related fatalities and injuries., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Yonas Kebede Lema et al.)

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human-machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers' responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers' gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over LOS sensors alone.

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object's velocity and direction of motion in the sensor's field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s -1 and a two-sigma confidence interval of [-0.0008 m s -1 , 0.0017 m s -1 ] for the axis-wise estimation of an object's relative velocity, and an RMSE of 0.0815 m s -1 and a two-sigma confidence interval of [0.0138 m s -1 , 0.0170 m s -1 ] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the 'yawning' and 'no_yawning' classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model.

Introduction: PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few studies have explored the real-world benefits of in-vehicle automation technologies, such as in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), for drivers with PD. These technologies hold potential to alleviate driving impairments, reduce errors, and improve overall performance, allowing individuals with PD to maintain their mobility and independence more safely and for longer periods. This preliminary study aimed to fill the gap in the literature by examining the impact of IVIS and ADAS on driving safety, as indicated by the number of driving errors made by people with PD in an on-road study., Methods: Forty-five adults with diagnosed PD drove a 2019 Toyota Camry equipped with IVIS and ADAS features (Toyota Safety Sense 2.0) on a route containing highway and suburban roads. Participants drove half of the route with the IVIS and ADAS systems activated and the other half with the systems deactivated., Results: The results suggest that systems that assume control of the driving task, such as adaptive cruise control, were most effective in reducing driving errors. Furthermore, individual differences in cognitive abilities, particularly memory, were significantly correlated with the total number of driving errors when the systems were deactivated, but no significant correlations were present when the systems were activated. Physical capability factors, such as rigidity and bradykinesia, were not significantly correlated with driving error., Discussion: Taken together, these results show that in-vehicle driver automation systems can benefit drivers with PD and diminish the impact of individual differences in driver cognitive ability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Giang, Zheng, Gibson, Patel, Ramirez-Zamora, Winter, Jeghers, Li and Classen.)

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS resulting in inadequate mental models. This may result in drivers misusing ADAS, or mistrusting the technologies, especially after encountering edge-case events (situations beyond the capability of an ADAS where the system may malfunction or fail) and may also adversely affect driver workload. Literature suggests mental models could be improved through exposure to ADAS-related driving situations, especially those related to ADAS capabilities and limitations. The objective of this study was to examine the impact of frequency and quality of exposure on drivers' understanding of Adaptive Cruise Control (ACC), their trust, and their workload after driving with ACC. Sixteen novice ACC users were recruited for this longitudinal driving simulator study. Drivers were randomly assigned to one of two groups - the 'Regular Exposure' group encountering 'routine' edge-case events, and the 'Enhanced Exposure' group encountering 'routine' and 'rare' events. Each participant undertook four different simulator sessions, each separated by about a week. Each session comprised a simulator drive featuring five edge-case scenarios. The study followed a mixed-subject design, with exposure frequency as the within-subject variable, and quality of exposure (defined by two groups) as the between-subject variable. Surveys measured drivers' trust, workload, and mental models. The results from the analyses using linear regression models revealed that drivers' mental models about ACC improve with frequency of exposure to ACC and associated edge-case driving situations. This was more the case for drivers who experienced 'rare' ACC edge cases. The findings also indicate that for those who encountered 'rare' edge cases, workload was higher and trust was lower than those who did not. These findings are significant since they underline the importance of experience and familiarity with ADAS for safe operation. While these findings indicate that drivers benefit from increased exposure to ACC and edge cases in terms of appropriate use of ADAS, and ultimately promise crash reductions and injury prevention, a challenge remains regarding how to provide drivers with appropriate exposure in a safe manner., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any departure from these conditions. As automation increases, a driver's attention may divert towards non-driving-related tasks (NDRTs), making transitions of control between the system and user more challenging. Safety features such as physiological monitoring thus become important with increasing vehicle automation. However, to date there has been no attempt to synthesise the evidence for the effect of NDRT engagement on drivers' physiological responses in Level 3 automation., Methods: A comprehensive search of the electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO, and IEEE Explore will be conducted. Empirical studies assessing the effect of NDRT engagement on at least one physiological parameter during Level 3 automation, in comparison with a control group or baseline condition will be included. Screening will take place in two stages, and the process will be outlined within a PRISMA flow diagram. Relevant physiological data will be extracted from studies and analysed using a series of meta-analyses by outcome. A risk of bias assessment will also be completed on the sample., Conclusion: This review will be the first to appraise the evidence for the physiological effect of NDRT engagement during Level 3 automation, and will have implications for future empirical research and the development of driver state monitoring systems., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Accurate perception and awareness of the environment surrounding the automobile is a challenge in automotive research. This article presents A3CarScene , a dataset recorded while driving a research vehicle equipped with audio and video sensors on public roads in the Marche Region, Italy. The sensor suite includes eight microphones installed inside and outside the passenger compartment and two dashcams mounted on the front and rear windows. Approximately 31 h of data for each device were collected during October and November 2022 by driving about 1500 km along diverse roads and landscapes, in variable weather conditions, in daytime and nighttime hours. All key information for the scene understanding process of automated vehicles has been accurately annotated. For each route, annotations with beginning and end timestamps report the type of road traveled ( motorway, trunk, primary, secondar y, tertiary, residential , and service roads), the degree of urbanization of the area ( city, town, suburban area, village, exurban and rural areas ), the weather conditions ( clear, cloudy, overcast , and rainy ), the level of lighting ( daytime, evening, night , and tunnel ), the type ( asphalt or cobblestones ) and moisture status ( dry or wet ) of the road pavement, and the state of the windows ( open or closed ). This large-scale dataset is valuable for developing new driving assistance technologies based on audio or video data alone or in a multimodal manner and for improving the performance of systems currently in use. The data acquisition process with sensors in multiple locations allows for the assessment of the best installation placement concerning the task. Deep learning engineers can use this dataset to build new baselines, as a comparative benchmark, and to extend existing databases for autonomous driving., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Background: Automated in-vehicle technologies, specifically in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), are increasingly common in today's cars. Previous studies illustrate benefits of using IVIS and ADAS to improve safety, convenience, and comfort in healthy older drivers. However, research is sparse on the feasibility of such technologies for medically at-risk drivers, such as those with Parkinson's disease (PD)., Objective: This study enrolled healthy volunteer drivers to examine the feasibility of the procedures and measures for evaluating the effects of IVIS and ADAS on their driving performance., Methods: During this feasibility study researchers compared drives completed with and without support of IVIS and ADAS, as participants drove a 2019 Toyota Camry XLE. The test vehicle was equipped with IVIS, ADAS, cameras, a telematics system, and sensors. Participants drove the road course supervised by a Driver Rehabilitation Specialist (DRS)., Results: Overall study procedures and vehicle equipment were feasible and provided sufficient data collection for measuring the impact of IVIS and ADAS on driving performance. Data observation by the DRS combined with data captured from cameras and telematics, facilitated comparisons to increase data reliability and validity., Conclusions: Feasibility study findings informed a randomized clinical trial, examining the use of IVIS and ADAS technologies as an intervention to support drivers with Parkinson's disease., Competing Interests: This study was supported by the National Institute on Disability, Independent Living, and Rehabilitation Research under award number 90IFRE0035. The authors have no conflicts of interest to declare.

The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way.

Objective: This field study examined the effects of adaptive cruise control (ACC) on mind wandering prevalence., Background: ACC relieves the driver of the need to regulate vehicle speed and following distance, which may result in safety benefits. However, if ACC reduces the amount of attentional resources drivers must devote to driving, then drivers who use ACC may experience increased periods of mind wandering, which could reduce safety., Methods: Participants drove a prescribed route on a public road twice, once using ACC and once driving manually. Mind wandering rates were assessed throughout the drive using auditory probes, which occurred at random intervals and required the participant to indicate whether or not they were mind wandering. Measures of physiological arousal and driving performance were also recorded., Results: No evidence of increased mind wandering was found when drivers used ACC. In fact, female drivers reported reduced rates of mind wandering when driving with ACC relative to manual driving. Driving with ACC also tended to be associated with increased physiological arousal and improved driving behavior., Conclusion: Use of ACC did not encourage increased mind wandering or negatively affect driving performance. In fact, the results indicate that ACC may have positive effects on driver safety among drivers who have limited experience with the technology., Application: Driver characteristics, such as level of experience with in-vehicle technology and gender, should be considered when investigating driver engagement during ACC use. Field research on vehicle automation may provide valuable insights over and above studies conducted in driving simulators.

Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.

ADAS and autonomous technologies in vehicles become more and more complex, which increases development time and expenses. This paper presents a new real-time ADAS multisensory validation system, which can speed up the development and implementation processes while lowering its cost. The proposed test system integrates a high-quality 3D CARLA simulator with a real-time-based automation platform. We present system experimental verifications on several types of sensors and testing system architectures. The first, open-loop experiment explains the real-time capabilities of the system based on the Mobileye 6 camera sensor detections. The second experiment runs a real-time closed-loop test of a lane-keeping algorithm (LKA) based on the Mobileye 6 line detection. The last experiment presents a simulation of Velodyne VLP-16 lidar, which runs a free space detection algorithm. Simulated lidar output is compared with the real lidar performance. We show that the platform generates reproducible results and allows closed-loop operation which, combined with a real-time collection of event information, promises good scalability toward complex ADAS or autonomous functionalities testing.

Driving in an adverse rain environment is a crucial challenge for vision-based advanced driver assistance systems (ADAS) in the automotive industry. The vehicle windshield wiper removes adherent raindrops that cause distorted images from in-vehicle frontal view cameras, but, additionally, it causes an occlusion that can hinder visibility at the same time. The wiper-occlusion causes erroneous judgments by vision-based applications and endangers safety. This study proposes behind-the-scenes (BTS) that detects and removes wiper-occlusion in real-time image inputs under rainy weather conditions. The pixel-wise wiper masks are detected by high-pass filtering to predict the optical flow of a sequential image pair. We fine-tuned a deep learning-based optical flow model with a synthesized dataset, which was generated with pseudo-ground truth wiper masks and flows using auto-labeling with acquired real rainy images. A typical optical flow dataset with static synthetic objects is synthesized with real fast-moving objects to enhance data diversity. We annotated wiper masks and scenes as detection ground truths from the collected real images for evaluation. BTS outperforms by achieving a 0.962 SSIM and 91.6% F1 score in wiper mask detection and 88.3% F1 score in wiper image detection. Consequently, BTS enhanced the performance of vision-based image restoration and object detection applications by canceling occlusions and demonstrated it potential role in improving ADAS under rainy weather conditions.

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.

This study examined the field effectiveness of General Motors advanced driver assistance and headlighting systems. A total of 8,311,707 Model Year 2013-2019 vehicles were matched to police-reported crashes from 12 states. The quasi-induced exposure method was used to compare system-relevant and system-irrelevant (control) crash counts for equipped and unequipped vehicles. Logistic regression was used to adjust for ten covariates. Results indicated fusion/radar Automatic Emergency Braking, camera Automatic Emergency Braking, and Forward Collision Alert systems reduced rear-end striking crashes by 45%, 38%, and 20%, respectively. When restricting data to crashes in which someone in the General Motors striking vehicle was injured, these reductions were elevated to 59%, 54%, and 31%, respectively, providing evidence of additional crash mitigation benefits. Similarly, the Lane Keep Assist with Lane Departure Warning and Lane Departure Warning (alone) systems provided 12% and 10% reductions in lane departure crashes, respectively, with corresponding benefits in the injury analysis increasing to 19% and 18%, respectively. The Lane Change Alert with Side Blind Zone Alert system reduced lane change crashes by 16%. Reverse Automatic Braking, Rear Cross Traffic Alert, Rear Park Assist, and Rear Vision Camera (where each of these systems generally included all of the preceding systems) produced, respectively, an 82%, 55%, 36%, and 24% reduction in backing crashes. For Front Pedestrian Braking, a non-significant 14% reduction was observed for the limited set of available pedestrian crash cases. Intellibeam (auto high beam headlighting), High-Intensity Discharge headlights, and the combination of these two systems provided 26%, 11%, and 32% reductions (relative to halogen headlights) in a combined set of (unlighted) nighttime animal, pedestrian, and bicyclist crashes, respectively. These results provide widespread evidence of the substantial crash avoidance and injury reduction opportunities afforded by the production systems evaluated, as well as identify untapped system opportunities for moving toward a zero crashes vision., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Crashes involving cyclists and pedestrians in Europe cause the deaths of about 7600 persons every year. Both cyclists and pedestrians are especially exposed in crashes with motorized vehicles and collisions with trucks can lead to severe injury outcomes. The two most frequent crash scenarios between trucks and these vulnerable road users (VRU) are: a) when the truck wants to turn right at an intersection, with a cyclist riding parallel and planning to cross the intersection and b) when a pedestrian crosses in front of the truck in perpendicular direction to the movement of the truck. Advanced Driver Assistance Systems (ADAS)-that are expected to prevent or mitigate these crashes-benefit from detailed information about the behavior of truck drivers. This study is a first exploration of this research area, with the aim to assess how drivers negotiate the encounters with VRUs in the two scenarios described above. Thirteen participants drove an instrumented truck on a test-track. After some baseline recordings, the drivers experienced two laps where they encountered a cyclist target and a pedestrian target crossing their path. The results show that the truck drivers adapted their kinematic and visual behavior in the laps where the VRU targets were crossing the intersection, compared to the baseline laps. The speed profiles of the drivers diverged approximately 30 m from the intersection and glances were directed more often towards front right and right, during the scenario with the cyclist in comparison to baseline laps. For the scenario with the pedestrian crossing, the drivers changed their speed about 14 m from the intersection and glances were directed more often towards the front center, compared to baseline laps. As a result, both the speed and distance from the intersection at the end of the maneuver were significantly different between VRU and baseline laps. Overall, the findings provide valuable information for the design of ADAS that warn the drivers about the presence of a cyclist travelling in parallel direction or that intervene to avoid a collision with a cyclist or pedestrian., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Level 2 (L2) driving automation systems that maintain latitudinal and longitudinal control of the vehicle decrease mental workload and result in drivers failing to monitor and respond to potential roadway hazards. This issue is potentially important for young drivers with symptoms of Attention Deficit Hyperactivity Disorder (ADHD) since they have known difficulties anticipating and mitigating potential hazards on the road, a skill which requires attention. The objective of this study is to investigate how the use of partially automated (L2) systems and manual systems impacts hazard anticipation and mitigation among young drivers with varying levels of ADHD symptomatology. Sixty-eight drivers, classified into two groups - high and low ADHD symptomatology-navigated twice through three scenarios on a driving simulator, once with an L2 and once with a manual system. The results indicated that: (i) the hazard anticipation skills of drivers with both high and low ADHD symptomatology were depressed in the L2 condition relative to the manual condition; (ii) the hazard mitigations skills of drivers with both high and low ADHD symptomatology were depressed in the L2 condition relative to the manual condition on two measures, but improved on a third measure; and (iii) the hazard anticipation and mitigation skills of drivers with high and low ADHD symptomatology were differentially impacted, both within and across the two levels of automation. Taken together, the results indicate the pernicious and often hard to predict consequences of higher levels of automation for different populations of younger drivers., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

The proliferation of Advanced Vehicle Technologies (AVTs) has generated both excitement and concern among researchers, policymakers, and the general public. An increasing number of driver assistance systems are already available in today's automobiles; many of which are expected to become standard. Therefore, synthesizing the available evidence specific to the safety of AVTs is critical. The goal of this scoping review was to summarize this evidence with a focus on AVTs that require some driver oversight (i.e., Levels 0-3 as per the Society of Automotive Engineers (SAE) levels of automation taxonomy). A scoping review of research literature on AVTs was conducted for studies up to March 2018. Inclusion criteria consisted of: any study with empirical data of AVTs that included male and female drivers aged 16 years and older, healthy people (i.e., without impairments), passenger vehicles, driving simulators and/or large databases with road safety information that could be analyzed for the purpose of examining AVTs (SAE Levels 0-3), as well as measures of driving outcomes. A total of 324 peer-reviewed studies from 25 countries met the inclusion criteria for this review with over half published in the last 5 years. Data was extracted and summarized according to the following categories: measures used to evaluate the effect of AVTs on road safety (objective) and driver perceptions of the technology (subjective), testing environment, and study populations (i.e., driver age). The most commonly reported objective measures were longitudinal control (50 %), reaction time (40 %), and lateral position (23 %). The most common subjective measures were perceptions of trust (27 %), workload (20 %), and satisfaction (17 %). While most studies investigated singular AVTs (237 of 324 studies), the number of studies after 2013 that examined 2 or more AVTs concurrently increased. Studies involved drivers from different age groups (51 %) and were conducted in driving simulators (70 %). Overall, the evidence is generally in favour of AVTs having a positive effect on driving safety, although the nature and design of studies varied widely. Our examination of this evidence highlights the opportunities as well as the challenges involved with investigating AVTs. Ensuring such technologies are congruent with the needs of drivers, particularly younger and older driver age groups, who are known to have a higher crash risk, is critical. With automotive manufacturers keen to adopt the latest AVTs, this scoping review highlights how testing of this technology has been undertaken, with a focus on how new research can be conducted to improve road safety now and in the future., (Copyright © 2020. Published by Elsevier Ltd.)

Self driving vehicles promise to bring one of the greatest technological and social revolutions of the next decade for their potential to drastically change human mobility and goods transportation, in particular regarding efficiency and safety. Autonomous racing provides very similar technological issues while allowing for more extreme conditions in a safe human environment. While the software stack driving the racing car consists of several modules, in this paper we focus on the localization problem, which provides as output the estimated pose of the vehicle needed by the planning and control modules. When driving near the friction limits, localization accuracy is critical as small errors can induce large errors in control due to the nonlinearities of the vehicle's dynamic model. In this paper, we present a localization architecture for a racing car that does not rely on Global Navigation Satellite Systems (GNSS). It consists of two multi-rate Extended Kalman Filters and an extension of a state-of-the-art laser-based Monte Carlo localization approach that exploits some a priori knowledge of the environment and context. We first compare the proposed method with a solution based on a widely employed state-of-the-art implementation, outlining its strengths and limitations within our experimental scenario. The architecture is then tested both in simulation and experimentally on a full-scale autonomous electric racing car during an event of Roborace Season Alpha. The results show its robustness in avoiding the robot kidnapping problem typical of particle filters localization methods, while providing a smooth and high rate pose estimate. The pose error distribution depends on the car velocity, and spans on average from 0.1 m (at 60 km/h) to 1.48 m (at 200 km/h) laterally and from 1.9 m (at 100 km/h) to 4.92 m (at 200 km/h) longitudinally.

Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will provide accurate trajectory-level weather information in real-time. The study used the SHRP2 Naturalistic Driving Study (NDS) video data and utilized several promising Deep Learning techniques, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). Python programming on the TensorFlow Machine Learning library has been used for training the Deep Learning models. The analysis was done on a dataset consisted of three weather conditions, including clear, distant fog and near fog. During the training process, two optimizers, including Adam and Gradient Descent, have been used. While the overall prediction accuracy of the DNN, RNN, LSTM, and CNN using the Gradient Descent optimizer were found to be around 85 %, 77 %, 84 %, and 97 %, respectively; much improved overall prediction accuracy of 88 %, 91 %, 93 %, and 98 % for the DNN, RNN, LSTM, and CNN, respectively, were observed considering the Adam optimizer. The proposed fog detection method requires only a single video camera to detect weather conditions, and therefore, can be an inexpensive option to be fitted in maintenance vehicles to collect trajectory-level weather information in real-time for expanding as well as updating weather-based Variable Speed Limit (VSL) systems and Advanced Traveler Information Systems (ATIS)., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Traffic congestion is more likely to lead to aggressive driving behavior that is associated with increased crash risks. Previous studies mainly focus on driving behavior during congestion when studying congestion effects. However, the negative effects of congestion on driving behavior may also affect drivers' post-congestion driving. To fill this research gap, this study examined the influence of traffic congestion on driver behavior on the post-congestion roads (i.e., the roads travelled right after congestion). Twenty-five subjects participated in a driving simulation study. They were asked to complete two trials corresponding to post-congestion and non-congestion conditions, respectively. Driver behavior quantified by driving performance measures, eye movement measures, and electroencephalogram (EEG) measures was compared between the two conditions. Ten features were selected from the measures with statistical significance. The selected features were integrated to characterize drivers' response patterns using a hierarchical clustering method. The results showed that driver behavior in post-congestion situations became more aggressive, more focused in the forward area but less focused in the dashboard area, and was associated with lower power of the β-band in the temporal brain region. The clustering results showed more aggressive and lack-of-aware response patterns while driving in post-congestion situations. This study revealed that traffic congestion negatively affected driver behavior on the post-congestion roads. Practical implications for driving safety education was discussed based on the findings from the present study., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: This study assessed older drivers' driving behavior when using longitudinal and lateral vehicle warning systems together., Background: Advanced driver assistance systems (ADAS) can benefit drivers of all ages. Previous research with younger to middle-aged samples suggests that safety benefits are not necessarily additive with additional ADAS. Increases in following distance associated with the use of forward collision warning (FCW) decreased when drivers also used lane departure warning (LDW), likely due to attending to the LDW more than the FCW., Method: The current study used a driving simulator to provide 128 older drivers experience with FCW and/or LDW system(s) during a ~25-min drive to gauge their usage's effects on driving performance and subjective workload., Results: There were no significant differences found in headway distance between older drivers who used different combinations of FCW and LDW systems, but those who used an FCW system showed significantly longer time-to-collision (TTC) when approaching the critical event than those who did not. Users of LDW systems did not show reductions in standard deviation of lane position. Analyses of subjective workload measures showed no significant differences between conditions., Conclusion: Findings suggest that FCW could increase older drivers' TTC over the course of a drive. Contrary to previous findings in younger samples, concurrent use of FCW and LDW systems did not adversely affect older drivers' longitudinal driving performance and subjective workload., Application: Potential applications of this research include the assessment of older drivers' use of vehicle warning systems and their effects on subjective workload.

Introduction: Automobile manufacturers are developing increasingly sophisticated driving automation systems. Currently, the highest level of automation available on the market is SAE Level 2, which provides sustained assistance for both lateral and longitudinal vehicle control. The purpose of this study was to evaluate how drivers' perceptions of what behaviors secondary to driving are safe while a Level 2 system is operating vary by system name., Methods: A nationally representative telephone survey of 2005 drivers was conducted in 2018 with questions about behaviors respondents perceived as safe while a Level 2 driving automation system is in operation. Each respondent was asked about two out of five system names at random for a balanced study design., Results: The name "Autopilot" was associated with the highest likelihood that drivers believed a behavior was safe while in operation, for every behavior measured. There was less variation observed among the other four SAE Level 2 system names when compared with each other. A limited proportion of drivers had experience with advanced driver assistance systems and fewer of these reported driving a vehicle in which Level 2 systems were available. Drivers reported that they would consult a variety of sources for information on how to use a Level 2 system., Conclusions: The names of SAE Level 2 driving automation systems influence drivers' perceptions of how to use them, and the name "Autopilot" was associated with the strongest effect. While a name alone cannot properly instruct drivers on how to use a system, it is a piece of information and must be considered so that drivers are not misled about the correct usage of these systems. Practical Applications: Manufacturers, suppliers, and organizations regulating or evaluating SAE Level 2 automated driving systems should ensure that systems are named so as not to mislead drivers about their safe use., (Copyright © 2019 National Safety Council and Elsevier Ltd. All rights reserved.)

Objective: Intersection collision is the most common type of vehicle accidents, and it is more complicated and has more variables compared to straight road collisions. Intersection collisions are also a major obstacle to practical application of self-driving technology. This calls for more studies and development of Intersection-Advanced Driver Assistance System (I-ADAS) which can be applied to various scenarios of intersection collisions. Method: In this study, NHTSA FARS and NASS-CDS DB from the period of 2013-2015 were used to analyze the circumstances and severity of damage in intersection collisions. With these analysis results, 17 possible vehicle collision scenarios were established based on the travel directions and relative positions of the two vehicles. The 17 accident scenarios were categorized into nine groups based on the travel direction and relative positions, and accident characteristics of each group, such as the severity of injuries, were analyzed. Results: Based on these characteristics, a method of qualitatively predicting and avoiding an intersection collision accident using a black box camera mounted on vehicles is introduced. Conclusion: When classifying the collision types, it was done with the consideration of making car-mounted camera-based intersection accident prediction and prevention possible. The intersection accident scenarios deduced for the purpose of the development of I-ADAS and self-driving system were analyzed regarding the severity of injuries and other factors. Based on these factors, possible methods to predict and prevent intersection accidents by using video footages and other data from the black box installed on cars were simply suggested.

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Advanced driver assistance systems (ADAS) have the potential to prevent crashes and reduce their severity. Forward collision warnings (FCW) are quickly becoming standard across vehicle lineups and may prevent frontal crashes by alerting drivers. Previous research has demonstrated the effectiveness of FCW for distracted drivers, but their effectiveness for other types of impairment remains unknown. Like distraction, drowsiness can impair driver response time and lead to crashes. The goal of the present study was to evaluate the effectiveness of FCW for moderately and severely drowsy drivers using a high-fidelity driving simulator. Drowsy drivers were divided into three warning conditions during a revealed stop vehicle forward collision event: An auditory alert, a haptic seat vibration, and a no warning baseline. Results indicate that FCW were effective at speeding drowsy driver response, but only when the drowsy drivers were looking away from the forward roadway at the onset of the event. These results have important implications for ADAS technology and driver state monitoring systems., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The use of advanced driver assistance systems and the transition towards semi-autonomous vehicles are expected to contribute to a lower frequency of motor accidents and to have a significant impact for the automobile insurance industry, as rating methods must be revised to ensure that risks are correctly measured. Telematics information and usage-based insurance research are analyzed to identify the effect of driving patterns on the risk of accident. This is used as a starting point for addressing risk quantification and safety for vehicles that can control speed. The effect of excess speed on the risk of accidents is estimated with a real telematics data set. Scenarios for a reduction of speed limit violations and the consequent decrease in the expected number of accident claims are shown. If excess speed could be eliminated, then the expected number of accident claims could be reduced to half of its initial value, applying the average conditions of the data used in this study. As a consequence, insurance premiums also diminish., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Objective : Drivers' use of lane departure warning and prevention systems is lower than use of other crash avoidance technologies and varies significantly by manufacturer. One factor that may affect use is how well a system prevents unintended departures. The current study evaluated the performance of systems that assist in preventing departures by providing steering or braking input in a 2016 Chevrolet Malibu, 2016 Ford Fusion, 2016 Honda Accord, and 2018 Volvo S90. These vehicles were selected because a prior observational study found that the percentage of privately owned vehicles that had lane departure prevention systems turned on varied among these 4 automakers. Method : In each vehicle, a test driver induced 40 lane drifts on left and right curves by steering the vehicle straight into the curve so that vehicles departed in the opposite direction and 40 lane drifts on straightaways by slight steering input to direct the vehicle to left and right lane markers. Results : Vehicles from automakers with higher observed lane departure prevention use rates (Volvo, Chevrolet) featured systems that provided steering input earlier and more often avoided crossing lane markers by more than 35 cm compared to vehicles from automakers with lower observed use rates (Ford, Honda). Conclusion : The study identified functional characteristics (i.e., timing of steering input, prevention of departures more than 35 cm) of lane departure prevention systems that were strongly associated with observed activation of these systems in privately owned vehicles. Although this relationship does not imply causation, the findings support the hypothesis that functional characteristics of lane departure prevention systems affect their use. Designers may be able to use these results to maximize driver acceptance of future implementations of lane departure prevention.

An underwater imaging system was investigated for automotive use in highly scattered underwater environments. The purpose of the system is the driver's information about hidden obstacles, such as stones, driftwood, open sewer hatches. A comparison of various underwater vision methods was presented by the way they are implemented, the range reached, and the cost of implementation. It has been experimentally shown that a conventional active system can provide a maximum visibility range of up to three light attenuation lengths. In most practical cases of turbid waters during floods, this corresponds to distances of about 1 meter. From the presented analysis it follows that advanced extended range imaging methods allow increasing of the visibility range up to 2 meters.

Driver support systems are intended to enhance driver performance and improve transportation safety. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the adoption of new in-vehicle technologies into the transportation system. In this study, a model of driver acceptance of driver support systems was developed. A conceptual driver acceptance model, including several components, was proposed based on a review of current literature. An empirical study was subsequently carried out using an online survey approach. The study collected data on participants' perceptions of two driver support systems (a fatigue monitoring system and an adaptive cruise control system combined with a lane-keeping system) in terms of attitude, perceived usefulness, and other components of driver acceptance. Results identified five components of driver acceptance (attitude, perceived usefulness, endorsement, compatibility, and affordability). The results also confirmed several mediating effects. The developed model was able to explain 85% of the variability in driver acceptance. The model provides an improved understanding how driver acceptance is formed, including which factors affect driver acceptance and how they affect it. The model can also help automakers and researchers to assess the design and estimate the potential use of a driver support system. The model could also be highly beneficial in developing a questionnaire to assess driver acceptance., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Older adults are the fastest growing segment of the driving population. While there is a strong emphasis for older people to maintain their mobility, the safety of older drivers is a serious community concern. Frailty and declines in a range of age-related sensory, cognitive, and physical impairments can place older drivers at an increased risk of crash-related injuries and death. A number of studies have indicated that in-vehicle technologies such as Advanced Driver Assistance Systems (ADAS) and In-Vehicle Information Systems (IVIS) may provide assistance to older drivers. However, these technologies will only benefit older drivers if their design is congruent with the complex needs and diverse abilities of this driving cohort. The design of ADAS and IVIS is largely informed by automotive Human Machine Interface (HMI) guidelines. However, it is unclear to what extent the declining sensory, cognitive and physical capabilities of older drivers are addressed in the current guidelines. This paper provides a review of key current design guidelines for IVIS and ADAS with respect to the extent they address age-related changes in functional capacities. The review revealed that most of the HMI guidelines do not address design issues related to older driver impairments. In fact, in many guidelines driver age and sensory cognitive and physical impairments are not mentioned at all and where reference is made, it is typically very broad. Prescriptive advice on how to actually design a system so that it addresses the needs and limitations of older drivers is not provided. In order for older drivers to reap the full benefits that in-vehicle technology can afford, it is critical that further work establish how older driver limitations and capabilities can be supported by the system design process, including their inclusion into HMI design guidelines., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Due to their disproportional representation in fatal crashes, younger and older drivers both stand to benefit from in-vehicle safety technologies, yet little is known about how they value such technologies, or their willingness to adopt them. The current study investigated older (aged 65 and greater; N=49) and younger (ages 18-23; N=40) adults' valuation of a blind spot monitor and asked if self-reported visual difficulties while driving predicted the amount participants were willing to pay for a particular system (BMW's Active Blind Spot Detection System) that was demonstrated using a short video. Large and small anchor values ($250 and $500, respectively) were used as between subjects manipulations to examine the effects of initial valuation, and participants proceeded through a short staircase procedure that offered them either the free installation of the system on their current vehicle or a monetary prize ($25-$950) that changed in value according to which option they had selected in the previous step of the staircase procedure. Willingness to use other advanced driver assistance systems (lane-departure warning, automatic lane centering, emergency braking, adaptive cruise control, and self-parking systems) was also analyzed, additionally controlling for prior familiarity of those systems. Results showed that increased age was associated with a higher valuation for the Active Blind Spot Detection System in both the large and small anchor value conditions controlling for income, gender, and technology self-efficacy. Older adults valued blind spot detection about twice as much ($762) as younger adults ($383) in the large anchor condition, though both groups' values were in the range for the current cost of an aftermarket system. Similarly, age was the most robust positive predictor of willingness to adopt other driving technologies, along with system familiarity. Difficulties with driving-related visual factors also positively predicting acceptance levels for adaptive cruise control and emergency braking systems. Results are discussed in comparison to older adults' willingness to pay for other home-based assistive technologies aimed at improving well-being and independence., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The interaction with Advanced Driver Assistance Systems has several positive implications for road safety, but also some potential downsides such as mental workload and automation complacency. Malleable attentional resources allocation theory describes two possible processes that can generate workload in interaction with advanced assisting devices. The purpose of the present study is to determine if specific analysis of the different modalities of autonomic control of nervous system can be used to discriminate different potential workload processes generated during assisted-driving tasks and automation complacency situations. Thirty-five drivers were tested in a virtual scenario while using head-up advanced warning assistance system. Repeated MANOVA were used to examine changes in autonomic activity across a combination of different user interactions generated by the advanced assistance system: (1) expected take-over request without anticipatory warning; (2) expected take-over request with two-second anticipatory warning; (3) unexpected take-over request with misleading warning; (4) unexpected take-over request without warning. Results shows that analysis of autonomic modulations can discriminate two different resources allocation processes, related to different behavioral performances. The user's interaction that required divided attention under expected situations produced performance enhancement and reciprocally-coupled parasympathetic inhibition with sympathetic activity. At the same time, supervising interactions that generated automation complacency were described specifically by uncoupled sympathetic activation. Safety implications for automated assistance systems developments are considered., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

A large portion of road traffic crashes occur at intersections for the reason that drivers lack necessary visual information. This research examined the effects of an audio-visual display that provides real-time sonification and visualization of the speed and direction of another car approaching the crossroads on an intersecting road. The location of red blinking lights (left vs. right on the speedometer) and the lateral input direction of beeps (left vs. right ear in headphones) corresponded to the direction from where the other car approached, and the blink and beep rates were a function of the approaching car's speed. Two driving simulators were linked so that the participant and the experimenter drove in the same virtual world. Participants (N = 25) completed four sessions (two with the audio-visual display on, two with the audio-visual display off), each session consisting of 22 intersections at which the experimenter approached from the left or right and either maintained speed or slowed down. Compared to driving with the display off, the audio-visual display resulted in enhanced traffic efficiency (i.e., greater mean speed, less coasting) while not compromising safety (i.e., the time gap between the two vehicles was equivalent). A post-experiment questionnaire showed that the beeps were regarded as more useful than the lights. It is argued that the audio-visual display is a promising means of supporting drivers until fully automated driving is technically feasible., (Copyright © 2016. Published by Elsevier Ltd.)

Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments., Competing Interests: The authors declare no conflict of interest.

Problem: The Adaptive Cruise Control is an Advanced Driver Assistance System (ADAS) that allows maintaining given headway and speed, according to settings pre-defined by the users. Despite the potential benefits associated to the utilization of ACC, previous studies warned against negative behavioral adaptations that might occur while driving with the system activated. Unfortunately, up to now, there are no unanimous results about the effects induced by the usage of ACC on speed and time headway to the vehicle in front. Also, few studies were performed including actual users of ACC among the subjects., Objectives: This research aimed to investigate the effect of the experience gained with ACC on speed and time headway for a group of users of the system. In addition, it explored the impact of ACC usage on speed and time headway for ACC users and regular drivers., Method: A matched sample driving simulator study was planned as a two-way (2×2) repeated measures mixed design, with the experience with ACC as between-subjects factor and the driving condition (with ACC and manually) as within-subjects factor., Results: The results show that the usage of ACC brought a small but not significant reduction of speed and, especially, the maintenance of safer time headways, being the latter result greater for ACC users, probably as a consequence of their experience in using the system., Summary: The usage of ACC did not cause any negative behavioral adaptations to the system regarding speed and time headway., Practical Applications: Based on this research work, the Adaptive Cruise Control showed the potential to improve road safety for what concerns the speed and the time headway maintained by the drivers. The speed of the surrounding traffic and the minimum time headway settable through the ACC seem to have an important effect on the road safety improvement achievable with the system., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.