Automated Driver Assistance Systems Research Articles

Development and application of a risk analysis methodology for road traffic accidents

Collision, rollover, swerving, and vehicle fire are road traffic accidents (RTAs) caused by driver, vehicle, and environment-related factors, resulting in diverse severity levels, such as property damages, injuries, and fatality. In addition, vagueness in detectability, e.g., close circuit television and driver admittance, culminates RTAs' risk analysis into a multifaceted problem. The present study performed root cause analysis to investigate RTAs, where the fishbone diagram categorized RTAs (e.g., collision and swerving) and identified primary (e.g., driver and environment) and secondary (e.g., overspeeding and tire burst) root causes. For 1158 km of intercity highways in Qassim (Saudi Arabia), fuzzy-failure mode and effect analysis evaluated 171 failure modes using 3500 RTAs' recorded over three years and knowledge of practice and experts. The study highlights collision, driver-related issues, and property damage as the leading category, root cause, and consequence. Comparing the baseline with the first realistic mitigation (speed camera installation) acknowledged a 53 % reduction in risk probability number. Automated driver assistance systems, rumble strips, variable speed limits, and increasing penalties for traffic violations hypothetically dropped 76 % of cumulative risk. The methodology handles limited data and subjective risk factors to help decision-makers minimize RTAs in Saudi Arabia and other jurisdictions.

A Multimodal Importance Sampling Approach for the Probabilistic Safety Assessment of Automated Driver Assistance Systems

Abstract In this article, we present a stochastic approach for the reliability evaluation of specific traffic scenarios as one component in the validation procedure of advanced driver assistance systems (ADAS). In this analysis, the control device is represented as a simulation model using software-in-the-loop technology. Specific inputs of this simulated controller are modeled as scalar random inputs. Based on a definition of a failure criterion, the well-known reliability method can be applied. In the present article, a variance-reduced importance sampling strategy for multiple failure regions is presented, which was developed for a scenario-based safety assessment framework.

Rain detection and removal from slowly varying background videos

Rain in bad weather becomes very challenging issue for automated driver assistance systems, security applications and many other computer vision algorithms. Number of rain removal algorithms have been proposed in these years but none of them has been able to increase the accuracy to large extent. In this paper a novel algorithm has been proposed where the slowly varying back ground scenes are taken for analysis. One part of the algorithm is based on the detection of rain pixels and the other part is removing them such that the original image must not lose its originality. Initially one background is modelled by taking the intensities of neighbourhoods of pixels. Then rain is detected by comparing each pixel with the modelled back ground followed by updating the background. The detected rain pixels are repaired with the help of some temporal neighbours. The results are promising to prove the work.

Different level automation technology acceptance: Older adult driver opinion

The increase in the number of older adult drivers in developed countries has raised safety concerns due to the decline in their sensory, motor, perceptual, and cognitive abilities which can limit their driving capabilities. Their driving safety could be enhanced by the use of modern Automated Driver Assistance Systems (ADASs) and might totally resolved by full driving automation. However, the acceptance of these technologies by older adult drivers is not yet well understood. Thus, this study investigated older adult drivers’ intention to use six ADASs and full driving automation through two questionnaires with 115 and 132 participants respectively in Rhode Island, USA. A four-dimensional model referred to as the USEA model was used for exploring older adult drivers’ technology acceptance. The USEA model included perceived usefulness, perceived safety, perceived ease of use, and perceived anxiety. Path Analysis was applied to evaluate the proposed model. The results of this study identified the important factors in older adult drivers’ intention to use ADASs and full driving automation, which could assist stakeholders in improving technologies for use by older drivers.

Stepping over the threshold linking understanding and usage of Automated Driver Assistance Systems (ADAS)

Automated Driver Assistance Systems (ADAS), which aim to enhance safety and comfort while driving, are becoming increasingly popular in vehicles today. However, ADAS are not yet operative in every situation due to technical limitations, and therefore do not cover all driving situations, traffic, weather and/or road conditions. In order for drivers to use these systems in a safe manner, they need to understand the different modes of operation, as well as the limitations of the systems, or they will not be able to build appropriate trust and adequate usage strategies.Therefore, the purpose of this study was to investigate the factors influencing user understanding of ADAS by implementing an Explanatory Sequential Mixed Methods design. This was done by triangulating data from a Naturalistic Driving (ND) study (132 vehicles) with explanations and reflections from in-depth interviews of purposefully selected participants (12 drivers from the vehicle pool) who were showing different usage patterns.The results show that users’ understanding is influenced by preconceptions about the system, as well as the perceived system performance and usefulness, leading to different levels of trust that affect the users’ engagement with the ADAS. It was found that the driver’s perception of a system does not just change over time, but changes through different situations presented, challenging the expected events and the users’ mental model of the interaction with the system. Therefore, to gain trust and appropriate usage strategies for the ADAS the user needs to overcome potentially negative experiences and challenge the current understanding of the ADAS, by stepping over the threshold.

Analysing the methods for determining coefficient of resistance to car wheels rolling

When simulating the operation of automatic or automated driver assistance systems, it is necessary to take into account the rolling resistance of the car wheels the moment the car begins to move on a slope, which necessitates the choice of a method for calculating the value of the coefficient of rolling resistance of the car wheels. Goal. The aim of the work is to determine a rational method for calculating the coefficient of rolling resistance of a car wheel based on the analysis of methods and approaches in relation to determining the coefficient of rolling resistance of car wheels. Methodology. The work was performed by the method of generalizing information using the method of analytical comparison and includes the analysis of information from various network resources, standards, scientific libraries and open bibliographic special sources. Results. The main result of this work is the conclusion that not every method of calculating the coefficient of rolling resistance is applicable when modeling the start of movement of a wheeled vehicle on the rise. Based on the analysis of methods for calculating the rolling resistance coefficient of a car wheel, recommendations are given on choosing the most rational method for modeling the start of a car standing on a mountainside. Scientific novelty. Prerequisites have been formed for further research related to modeling a wheeled vehicle upgrading. The analyzed information from open scientific sources indicates that the chosen direction is covered quite widely in the specialized technical literature, but requires additional research in connection with the peculiarities of the work processes that occur the moment the car is standing on the side of the mountain. Practical significance. The results of the study can be used to expand theoretical information, which is a component of the course on car theory in teaching undergraduate and graduate students in the field of the automotive industry. The materials of the work can also be used to form mathematical models related to the description of rolling resistance of car wheels tires.

Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS) -Naturalistic Driving Study for ADAS evaluation

Automated Driver Assistance Systems (ADAS) are designed to support the driver and enhance the driving experience. Due to ADAS limitations associated with the driving context, the intended use of ADAS functions is often non-transparent for the end-user. The system performance capabilities affected by the continuously changing driving context influence ADAS usage. However, the cumulative effect of the driving context on driver behavior and ADAS usage is insufficiently covered in the ongoing research. This paper aims to investigate and understand how the driving context affects the use of ADAS. Throughout this research, data from a Naturalistic Driving (ND) study was collected and analyzed. The analysis of the ND data helped to register how drivers use ADAS in different driving conditions and indicated several issues associated with ADAS usage. To be able to clarify the outcomes of quantitative sensor-based data analysis, an explanatory sequential mixed-method design was implemented. The method facilitated the subsequent design of qualitative in-depth interviews with the drivers. The combined data analysis allowed a holistic interpretation and evaluation of the findings regarding the effect of the driving context on ADAS usage. The findings warrant consideration of the driving context as a key factor enabling the effective development of ADAS functions.

Design of a data-driven communication framework as personalized support for users of ADAS.

Recently the automotive industry has made a huge leap forward in Automated Driver Assistance Systems (ADAS) development, increasing the level of driving processes automation. However, ADAS design does not imply any individual support to the driver; this results in a poor understanding of how the ADAS works and its limitations. This type of driver uncertainty regarding ADAS performance can erode the user's trust in the system and result in decreasing situations when the system is in use. This paper presents the design of a data-driven communication framework that can utilize historical and real-time vehicle data to support ADAS users. The data-driven communication framework aims to illustrate the ADAS capabilities and limitations and suggests effective use of the system in real-time driving situations. This type of assistance can improve a driver's understanding of ADAS functionality and encourage its usage.

Naturalistic driving study for Automated Driver Assistance Systems (ADAS) evaluation in the Chinese, Swedish and American markets.

In recent years, Automated Driver Assistance Systems (ADAS) have received great promotion and acceptance in the European market. However, transferring ADAS to other markets may affect driver behavior due to the cultural and contextual differences in various markets. Methods used for capturing these differences are based on subjective data collection. This study shows how vehicle data collected in the ND study helps to identify and investigate further the differences in driver behavior and the driving context in the Chinese, Swedish and US markets. This paper discusses a better way to consider the infrastructural and cultural differences in ADAS design.

OPEDD: Offroad Pedestrian Detection Dataset

The detection of pedestrians plays an essential part in the development of automated driver assistance systems. Many of the currently available datasets for pedestrian detection focus on urban environments. State-of-the-art neural networks trained on these datasets struggle in generalizing their predictions from one environment to a visually dissimilar one, limiting the use case to urban scenes. Commercial working machines like tractors or excavators make up a substantial share of the total number of motorized vehicles and are often situated in fundamentally different surroundings, e.g. forests, meadows, construction sites or farmland. In this paper, we present a dataset for pedestrian detection which consists of 1018 stereo-images showing varying numbers of persons in differing non-urban environments and comes with manually annotated pixel-level segmentation masks and bounding boxes.

Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case

AbstractAutomotive systems are changing rapidly from purely mechanical to smart, programmable assistants. These systems react and respond to the driving environment and communicate with other subsystems for better driver support and safety. However, instead of supporting, the complexity of such systems can result in a stressful experience for the driver, adding to the workload. Hence, a poorly designed system, from a usability and user experience perspective, can lead to reduced usage or even ignorance of the provided functionalities, especially concerning Adaptive Driver Assistance Systems.In this paper, the authors propose a combined design approach for user behavior evaluation of such systems. At the core of the design is a mixed methods approach, where objective data, which is automatically collected in vehicles, is augmented with subjective data, which is gathered through in- depth interviews with end-users. The aim of the proposed methodology design is to improve current practices on user behavior evaluation, achieve a deeper understanding of driver's behavior, and improve the validity and rigor of the named results.

Vision-Based Steering Control, Speed Assistance and Localization for Inner-City Vehicles.

Autonomous route following with road vehicles has gained popularity in the last few decades. In order to provide highly automated driver assistance systems, different types and combinations of sensors have been presented in the literature. However, most of these approaches apply quite sophisticated and expensive sensors, and hence, the development of a cost-efficient solution still remains a challenging problem. This work proposes the use of a single monocular camera sensor for an automatic steering control, speed assistance for the driver and localization of the vehicle on a road. Herein, we assume that the vehicle is mainly traveling along a predefined path, such as in public transport. A computer vision approach is presented to detect a line painted on the road, which defines the path to follow. Visual markers with a special design painted on the road provide information to localize the vehicle and to assist in its speed control. Furthermore, a vision-based control system, which keeps the vehicle on the predefined path under inner-city speed constraints, is also presented. Real driving tests with a commercial car on a closed circuit finally prove the applicability of the derived approach. In these tests, the car reached a maximum speed of 48 km/h and successfully traveled a distance of 7 km without the intervention of a human driver and any interruption.

Recommendations Supporting Situation Awareness in Partially Automated Driver Assistance Systems

It is believed that automated driving is able to fulfill the demand for comfort and safety in traffic. Recent developments have been able to take over all parts of the driving task in specific situations. However, it remains to the driver to monitor the system's behavior upon errors and to intervene in the case of critical situations. On account of the automation, the driver may not be able to overlook the whole situation in the same way he would do while driving manually. In addition to that, the driver's motivation to perform secondary tasks while being driven enhances these challenges. The research activities presented in this paper are concentrated on the development and the evaluation of certain human–machine interface (HMI) mechanisms. It is hypothesized that such mechanisms have a positive impact on driver's situation awareness (SA) and resulting driving safety (DS). The evaluation of these mechanisms occurred in a driving simulator study. Based on the evaluation results, a system presenting the secondary task's display in the vehicle's head-up display is proposed. Thereby, the driver is expected to be able to keep the vehicle's environment in his peripheral field of view without being distracted too much. At the same time, relevant elements to operate the secondary task should be located on the steering wheel. This is expected to reduce the time needed for steering reactions, as drivers keep at least one hand on the steering wheel. The results suggest that such a system design appears reasonable in order to enhance driver's SA and DS while driving partially automated.

Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles.

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for ACC systems that can reduce mode confusion. To meet this requirement, this paper presents a new human-automation interaction design methodology in which the compatibility of the machine and interface models is determined using the proposed criteria, and if the models are incompatible, one or both of the models is/are modified to make them compatible. To investigate the effectiveness of our methodology, we designed two new interfaces by separately modifying the machine model and the interface model and then performed driver-in-the-loop experiments. The results showed that modifying the machine model provides a more compact, acceptable, effective, and safe interface than modifying the interface model.

Editorial

Recently, considerable technological progress has been made in the field of AutomatedDriver Assistance Systems (ADAS). Electronic devices inform or support the driver inaccident-prone driving situations, in order to improve the critical task of driving a motorvehicle. Potentially, ADAS offers important advantages for road transportation: increasedcontrol with respect to the speed and the position of vehicles on the road is important forestablishing homogeneous traffic flows and reducing the number of accidents. As suchADAS is assumed to have a positive impact on the use of road infrastructure and trafficsafety (Boussuge & Valade, 1994). Moreover, this could lead to a reduction of energy useand polluting gas emissions (Barth, 1995; Michaelian & Browand, 2000). As soon as parts ofor the whole driving task are supported and/or executed automatically by ADAS, vehicledriving could become more comfortable and more convenient as compared to today’s manualdriving (Stevens, 1997; Hoedemaeker, 1999). These expectations imply a high potential inindividual and societal advantages. In various countries, therefore, transport policy makers areincreasingly interested in the automation of vehicle driving tasks. However, current policydevelopment regarding ADAS is highly complicated by, among others, much uncertainty onfuture ADAS development and implementation in terms of whether ADAS implementationwill contribute to or conflict with transport policy goals, and the basic societal conditionsrequired for ADAS implementation (Marchau, 2000)