Development Of Driver Assistance Systems Research Articles

Integrated Thermomechanical Analysis of Tires and Brakes for Vehicle Dynamics and Safety

The accurate prediction of tire and brake thermomechanical behavior is crucial for various applications in the automotive industry, including vehicle dynamics analysis, racing performance optimization, and driver assistance system development. The temperature of the brakes plays a crucial role in determining the performance of rubber by altering its temperature. This change impacts the rim and the air within the tire, leading to variations in temperature and tire pressure, which consequently affect the vehicle’s overall performance. Traditionally, these components have been modeled separately, neglecting the crucial thermal interaction between them, thereby losing a lot of important information from the outside that influences the tire. This paper presents a novel method that overcomes this limitation by coupling the thermomechanical models of the tire and brake, enabling a more comprehensive understanding of their combined behavior. Therefore, the present work could be an interesting starting point to understand how a control system can be influenced by the thermodynamic of the wheel–brake system.

Data Quality as Key to Driver Assistance System Development

Data Quality as Key to Driver Assistance System Development

Driver's visual fixation attention prediction in dynamic scenes using hybrid neural networks

The traffic driving scenario is dynamically varying and experienced drivers can allocate visual attention effectively in brief time intervals, focusing on prominent targets and areas in advance, thus ensuring safe driving. Therefore, it is significant to research the behavior of driver's visual attention allocation in the development of driver assistance systems and autonomous vehicles. Recently, inspired by top-down and bottom-up attention mechanisms, most visual attention models based on neural networks have been proposed. However, these models tend to yield highly distributed predictions for the specific driving scenario, and have relatively low prediction accuracy, making them unfeasible for being used in practice. Hence, to overcome these challenges, we propose a driver's visual attention model (DVAM) built on the encoder-decoder architecture with the utilization of the convolutional neural network (CNN) and the recurrent neural networks (RNN). Specifically, the model leverages the temporal and spatial dimension information to better mimic realistic dynamic driving, and extract abundant feature information, thus ensuring that the final predictions are authentic and effective. Then, extensive experiments demonstrate that our suggested DVAM achieves better robustness and precision in predicting driver attention to areas or targets compared to state-of-the-art methods on the DR(eye)VE dataset. Finally, our model is deployed on the TDV dataset to validate its generalization capabilities, which prove the potential for field adaptation.

Research and evaluation on comprehensive obstacle-avoiding behavior for unmanned vehicles based on the interval neutrosophic number multiple attribute group decision making

With the advent of the information age, the development direction of automobiles has gradually changed, both from the domestic and foreign policy support attitude, or from the actual actions of the automotive industry and scientific research institutes’ continuous efforts, it is not difficult to see that driverless vehicle. At this time, the testing and evaluation of the intelligent behavior of driverless vehicles is particularly important. It is particularly important not only to regulate the intelligent behavior of unmanned vehicles, but also to promote the key It can not only regulate the intelligent behavior of unmanned vehicles, but also promote the improvement of key technologies of unmanned vehicles and the research and development of driver assistance systems. The evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is often considered as a multi-attribute group decision making (MAGDM) problem. In this paper, the EDAS method is extended to the interval neutrosophic sets (INSs) setting to deal with MAGDM and the computational steps for all designs are listed. Then, the criteria importance through intercriteria correlation (CRITIC) is defined to obtain the attribute’s weight. Finally, the evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is given to demonstrate the interval neutrosophic number EDAS (INN-EDAS) model and some good comparative analysis is done to demonstrate the advantages of INN-EDAS.

ILDM: An Interoperable Graph-Based Local Dynamic Map

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development.

ResSCNN: A semantic segmentation method for fast processing of large-scale input

With the development of driver-assistance systems and driverless cars, vehicles are becoming more and more intelligent. However, today’s intelligent vehicles rely more on large sensors to sense the environment, so it is becoming more and more important for vehicles to be able to understand road information, and semantic segmentation has developed greatly. We find that the current mainstream semantic segmentation model is slow and inaccurate in the case of large scale input images. In this paper, we combine the large-scale input Fast-SCNN with the residual module to construct the residual semantic segmentation convolutional neural network (ResSCNN), after the experimental test, when the input scale is 1024 × 2048 px, yielding an accuracy of 68.4% mean intersection over union at 123.1 frames per second on Cityscapes dateset. Our network has achieved better results than the previous mainstream methods.

About the project developing “MIPRA” – the intelligent planner in the state space for vehicles, tractors, and robots based on the architectural solutions of the Mivar systems for traffic enforcement

The task of creating a logical “strong” artificial intelligence (AI) to be used as new decision-making systems for autonomous vehicles, tractors, and robotic systems draws increasing attention of scientists around the world. Essentially, it is the creation of “brains” for vehicles and any other transport systems, including cyber-physical systems. The mivar technologies of logical AI allowed solving many problems at a qualitatively new level and reducing the decision-making time from billions of years to hundredths of seconds, which enables real-time vehicle control. This paper shows how the application of mivar products (mivar expert systems) allows solving another class of problems – state-space planning (STRIPS planning). In turn, the solutions of these problems enable the development of driver assistance systems at a new level and creation of a fully autonomous vehicle.

A Novel Approach to Sensorimotor Skill Acquisition Utilizing Sensory Substitution: A Driving Simulation Study

The aim of this study is to demonstrate the potential of sensory substitution/augmentation (SS/A) techniques for driver assistance systems in a simulated driving environment. Using a group-comparison design, we examined lane-keeping skill acquisition in a driving simulator that can provide information regarding vehicle lateral position by changing the binaural balance of auditory white noise delivered to the driver. Consequently, lane-keeping accuracy was significantly degraded when the lower visual scene (proximal part of the road) was occluded, suggesting it conveyed critical visual information necessary for lane keeping. After 40 minutes of training with auditory cueing of vehicle lateral position, lane-keeping accuracy returned to the baseline (normal driving) level. This indicates that auditory cueing can compensate for the loss of visual information. Taken together, our data suggest that auditory cueing of vehicle lateral position is sufficient for lane-keeping skill acquisition and that SS/A techniques can potentially be used for the development of driver assistance systems, particularly for situations where immediate time-sensitive actions are required in response to rapidly changing sensory information. Although this study is the first to apply SS/A techniques to driver assistance, further studies are however required to establish the generalizability of the findings to real-world settings.

Location-based analysis of car-following behavior during braking using naturalistic driving data

This study investigates the car-following behavior during braking at intersections and segments. Car-following events were extracted from a naturalistic driving dataset, mapped using ArcGIS, and analyzed to differentiate between the intersection- and segment-related events. The intersection-related events were identified according to an intersection influence area, which was estimated based on the stopping sight distance and the speed limit. Five behavioral measures were quantified based on exploring the probability density functions (PDF) for intersection- and segment-related events. The results showed that there were significant differences between the PDFs of the measures for both cases. Moreover, it was indicated that drivers tend to be more aggressive at intersections compared with segments. Thus, it is crucial to consider the driver’s location when investigating driver behavior. The quantified behavioral measures are a rich data source that can be used for car-following microscopic modeling, surrogate safety analysis, and driver assistance systems development.

Virtual reality for automotive radars

Car manufacturers spend quite a lot on the development of driver assistance systems and subsequently on autonomous driving functionality. To ensure the safety and reliability of these functions meet industrial standards it is necessary to verify and validate their functionality. While tests on the road are still the ultimate evidence of correct operation they are associated with huge efforts and risks. Therefore, they have to be complemented by other means like simulations and tests on specialised testbeds. For the latter the car’s sensors have to be stimulated in a way that they perceive a desired – but only virtual – environment. An important type of sensor in cars is the radar due to its various advantages. This article describes the development of a stimulator generating virtual radar targets in order to enable the testing of autonomous driving functions.

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.

Development of a Driver Lateral Control Model by Integrating Neuromuscular Dynamics Into the Queuing Network-Based Driver Model

This paper describes the development of a novel driver lateral control model by integrating the driver's neuromuscular dynamics into the queuing network (QN)-based driver lateral control model. Experimental results from 16 participants in a driving simulator show that, compared to the QN-based model, the proposed model performs better, and its performance is closer to that of drivers when a vehicle runs at a relatively high speed. The proposed model not only has the advantages of the models based on a cognitive architecture but also captures the dynamic interaction between the vehicular steering system and the driver's neuromuscular system. Thus, it can better represent driver lateral control and has greater value in supporting the development of driver assistance systems.

A Review of Research on Driving Styles and Road Safety.

The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety. Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking. A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety. A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and sociocultural factors influencing driving style were reviewed. Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations. There is a considerable potential for increasing road safety by means of behavior modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles. Knowledge about driving styles can be applied in programs for modifying driver behavior and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems.

Driver assistance systems to improve active safety in the closed-loop system driver-vehicle-surroundings

This paper deals with the analysis of the closed–loop system consisting of driver, vehicle and the surroundings in critical driving situations. This study was geared towards analysing typical driver errors based upon driving situations registered frequently in accident statistics; this analysis should then be used for the development of assistance systems to optimize the driver's actions. The selected driving manoeuvres were simulated on the Daimler–Benz driving simulator with real drivers in order to reveal typical errors. The results of these tests formed the basis for the development of driver assistance systems. The results of the efficiency and acceptance studies concerning the braking and steering assistance developed by Daimler–Benz are presented here.

Driving Behavior and Simulator Sickness While Driving the Vehicle in the Loop: Validation of Longitudinal Driving Behavior

The Vehicle in the Loop (VIL) is a simulator, which combines real driving experience with the replicability and safety of simulators. In the VIL test setup a real test vehicle is combined with a virtual testing environment which is displayed to the user via a head-mounted display (HMD). In theory, this simulation concept renders the VIL uniquely suited to the development and evaluation of numerous automotive applications, including driver assistance systems. Aiming to assess the extent to which the VIL elicits realistic driving responses, a validation study was performed. In this first validation study the focus was on longitudinal driving behavior. 44 participants performed five common traffic maneuvers in reality and the VIL setup. Simulator sickness was assessed with the simulator sickness questionnaire (SSQ). Descriptive and inferential analyses of the data showed that the VIL achieves relative validity concerning brake pressure and reaction times and absolute validity concerning the steering angle. However, subjects showed longer reaction times and accelerated more smoothly while driving the VIL. One possible explanation for these results could be the presence of simulator sickness. Overall, the study indicates that the VIL represents a suitable testing method for the evaluation and development of driver assistance systems. The study also provided clear indications for the future development of the VIL.

The ergonomic value of a bidirectional haptic interface when driving a highly automated vehicle

Advances in technology have fueled the development of driver assistance systems. Even today, these systems can take over parts of the driving task. However, the interface becomes more and more complex with an increasing number of functions. One way to reduce such complexity is to venture the haptic channel. While haptic feedback in lateral direction is comparatively easy to realize via the steering wheel, the longitudinal direction forms a challenge. With conventional control elements, that is, pedals, haptic interaction can only be partially realized (this is due to the division of accelerator and brake pedals). Haptic signals, like forces added to the accelerator pedal, can only transmit information regarding the amount of acceleration, not the desired deceleration. In this context, two-dimensional control elements show great potential regarding future highly automated vehicle driving. Therefore, an experiment conducted at the Institute of Ergonomics of the Technische Universitat Munchen investigated the influence of haptic feedback of assistance systems on driving performance when using an active side stick as control element. Additionally, the impact of vehicle vibrations and accelerations were explored. Besides objective performance data, subjective assessment was also reported. The results show that adding assistance significantly improves driving performance. Moreover, subjective ratings indicate a reduction in workload. Accelerations and vibrations, however, had no verifiable effect on the driving performance. This fact was confirmed by the subjects' subjective assessment. This paper shows that two-dimensional control elements can be a reasonable alternative to steering wheel and pedals when driving a highly automated vehicle.

Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach

Recently, improvements in sensing, communicating, and computing technologies have led to the development of driver-assistance systems (DASs). Such systems aim at helping drivers by either providing a warning to reduce crashes or doing some of the control tasks to relieve a driver from repetitive and boring tasks. Thus, for example, adaptive cruise control (ACC) aims at relieving a driver from manually adjusting his/her speed to maintain a constant speed or a safe distance from the vehicle in front of him/her. Currently, ACC can be improved through vehicle-to-vehicle communication, where the current speed and acceleration of a vehicle can be transmitted to the following vehicles by intervehicle communication. This way, vehicle-to-vehicle communication with ACC can be combined in one single system called cooperative adaptive cruise control (CACC). This paper investigates CACC by proposing a novel approach for the design of autonomous vehicle controllers based on modern machine-learning techniques. More specifically, this paper shows how a reinforcement-learning approach can be used to develop controllers for the secure longitudinal following of a front vehicle. This approach uses function approximation techniques along with gradient-descent learning algorithms as a means of directly modifying a control policy to optimize its performance. The experimental results, through simulation, show that this design approach can result in efficient behavior for CACC.

Driver's various information process and multi-ruled decision-making mechanism: a fundamental of intelligent driving shaping model

The most difficult but important problem in advance driver assistance system development is how to measure and model the behavioral response of drivers with focusing on the cognition process. This paper describes driver's deceleration and acceleration behavior based on driving situation awareness in the car–following process, and then presents several driving models for analysis of driver's safety approaching behavior in traffic operation. The emphasis of our work is placed on the research of driver's various information process and multi-ruled decision-making mechanism by considering the complicated control process of driving; the results will be able to provide a theoretical basis for intelligent driving shaping model.

Interaction of Human, Machine, and Environment in Automated Driving Systems

In view of the increase in traffic volume and growing demands on traffic, alternative mobility concepts such as automation of the driving task are increasingly being investigated. In line with this course of development project KONVOI was initiated, where an interdisciplinary research team carried out the electronic coupling of trucks. Because the technical feasibility of such automated systems had already been proved, project KONVOI focused on quantifying the impact of automated systems on traffic and on the interaction between humans and machines. Technical development of the system took place with preferential consideration of the human being. The KONVOI system was analyzed for acceptance and the potential workload by using test people in a driving simulator. The test results had a direct effect on the technical development of the system. After approval of the prototype, the traffic effects of the KONVOI system were examined in the context of real German highways. The results of this impact analysis served as a comparison of the developmental concept and evaluation of the system. Open questions concerning the effects of the KONVOI system on car drivers were investigated through additional test drives in the driving simulator. In project KONVOI, an automated system was run in real traffic for the first time in Europe. This testing and the strong consideration of the human being in the development process have provided new insights into the development of driver assistant systems and automated driving systems.

Adapted Human Machine Interaction concept for Driver Assistance Systems DrivEasy

AbstractThanks to the important improvements in perception technologies as well as computing efficiency the development of Driver Assistance Systems is now a real growing market. Nevertheless these new functions that interact with the drivers are not without acceptance problems from the users. The DrivEasy concept that is presented within this paper is proposing adaptive solutions taking care about the current and contextual situations to improve the efficiency of such systems.