Development Of Advanced Driver Assistance Systems Research Articles

Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing

The rapid evolution of Advanced Driver Assistance Systems (ADAS) has transformed the automotive landscape, necessitating a shift towards more customer-centric development strategies. This paper explores the integration of predictive analytics with Customer Relationship Management (CRM) data to foster innovations in ADAS development. By harnessing insights from customer interactions, preferences, and feedback, manufacturers can anticipate market demands and tailor ADAS features to enhance user experience. Through a comprehensive analysis of case studies and industry practices, we demonstrate how predictive analytics can improve decision-making processes, facilitate the identification of emerging trends, and optimize resource allocation. The findings underscore the potential of leveraging CRM data to drive customer- focused innovations, ultimately resulting in enhanced vehicle safety, satisfaction, and competitive advantage in the automotive sector.

Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car.

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

Drowsiness Detection in the Advanced Driver-Assistance System using YOLO V5 Detection Model

The development of Artificial Intelligence (AI) in the automobile industry, particularly in the Advanced Driver-Assistance System (ADAS), has been increasing rapidly in recent years. One of the essential features of ADAS is the drowsiness alert system, which monitors the state of the driver's eyes. This article presents a study on the development of ADAS that focuses on drowsiness detection using deep learning through a Convolutional Neural Network (CNN) approach. The study utilized the YOLO V5 model for object detection, which was trained using custom datasets containing images annotated with two labels: "Mengantuk" (Drowsy) and "Bangun" (Awake) The goal was to recognize whether the driver was drowsy or awake. The results of the study showed that the modified YOLO V5 CNN Model had high accuracy based on evaluation metrics in terms of accuracy, precision, and recall in detecting drowsiness around the area of the eyes, with a Mean Average Precision (mAP) of 99.5% and an F1-Score of 99.8%. For better understanding and visualization, the model was tested using real-time detection through a web camera, using Jetson Nano as the inference device. The model detected drowsiness in real time, with a confidence rate of 80% to 97%

Modelling and performance analysis of free body dynamics of electric vehicles

The modelling and performance analysis of free body dynamics in electric vehicles (EVs) plays a crucial role in understanding and optimizing the vehicle's behavior and performance. This research focuses on accurately modelling the forces and motions acting on the vehicle body, excluding the powertrain components. By considering factors such as vehicle weight, suspension characteristics, tire properties, and aerodynamic forces, a comprehensive mathematical model is developed. This model enables the simulation and analysis of the vehicle's behavior under various operating conditions. The performance analysis involves evaluating key metrics such as vehicle response, stability limits, and ride comfort. The proposed system is compared with the existing electric vehicle in the market. The findings from this research contribute to the design and development of EVs with improved handling, stability, and energy efficiency. Additionally, they inform the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. Overall, the modelling and performance analysis of free body dynamics in EVs supports the advancement of sustainable and efficient transportation systems.

A Survey of Automotive Radar and Lidar Signal Processing and Architectures

In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Radar and lidar are crucial in this framework, since they allow sensing of vehicle’s surroundings. In such a scenario, it is necessary to master these sensing systems, and knowing their similarities and differences is important. Due to ADAS’s intrinsic real-time performance requirements, it is almost mandatory to be aware of the processing algorithms required by radar and lidar to understand what can be optimized and what actions can be taken to approach the real-time requirement. This review aims to present state-of-the-art radar and lidar technology, mainly focusing on modulation schemes and imaging systems, highlighting their weaknesses and strengths. Then, an overview of the sensor data processing algorithms is provided, with some considerations on what type of algorithms can be accelerated in hardware, pointing to some implementations from the literature. In conclusion, the basic concepts of sensor fusion are presented, and a comparison between radar and lidar is performed.

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

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

Testbed for industrial advanced driver assistance systems

Factories and industrial warehouses are environments in which accidents can be easily produced. People on foot work in the same place where heavy machinery is operating. Therefore, when accidents occur, they frequently have severe consequences. To reduce the number of accidents and their effects, there are strict regulations on the workplace and workers participate in regular training activities. In recent years, there has been a great evolution in Advanced Driver Assistance Systems (ADAS) specially in public road vehicles. With the proper design, these systems may also help to improve safety in industrial environments. For example, an ADAS may warn industrial drivers about nearby pedestrians. Nevertheless, the development of ADAS in this context is complex because industrial environments and their machines are very heterogeneous. Hence, this paper describes a testbed developed to assess the design of ADAS for industrial vehicles. The testbed includes all the elements needed to evaluate an industrial ADAS: hardware, an event management system, a simulator of a warehouse and, an evaluation methodology. To determine the effectiveness of the testbed, the assessment of an ADAS designed to warn industrial drivers about nearby obstacles has also been performed. The assessment includes a subjective evaluation of the testbed and of the cognitive load generated during the evaluation. The results of this assessment are very promising. They show that the testbed is realistic and that it is effective for ADAS designers to analyse the reactions of drivers to the signals produced by the assistance systems under evaluation.

Metamorphic testing of Advanced Driver-Assistance System (ADAS) simulation platforms: Lane Keeping Assist System (LKAS) case studies

Simulation-based testing is essential when developing Advanced Driver-Assistance Systems (ADASs) and autonomous driving (AD) systems, producing fast, high-quality test results, at relatively low cost. However, simulation testing relies on the quality of the ADAS simulation platform: If the simulation platform is faulty, then the simulation results may be incorrect, and hence useless. However, because of the lack of suitable test oracles — mechanisms to determine the correctness of the software output or behavior — it can be too difficult (or expensive) to verify or validate ADAS/AD simulation platforms, a situation known as the oracle problem. To alleviate the oracle problem and better understand ADAS simulation software. We develop geometric-transformation-based metamorphic testing approaches, and report on empirical studies conducted on the verification and validation (V&V) of three popular simulation platforms for ADAS development: Simulink, CarMaker and 51Sim-One Cloud. Our examination focused on the platforms’ Lane Keeping Assist Systems (LKASs). When tested with ordinary (traditional) test cases, no issues were identified on any simulation platform. However, after applying geometric-transformation-based metamorphic testing, issues were revealed, some of which were later confirmed by the MATLAB and IPG Automotive teams. To the best of our knowledge, this paper is the first to report on real bugs and issues in ADAS simulation platforms. Our research shows the simplicity, effectiveness and applicability of the proposed approach for ADAS simulation testing. This paper also provides successful examples of incorporating metamorphic testing into the testing of ADAS standards and protocols, and shows how practitioners can design effective MRs inspired by using the symmetry metamorphic relation pattern. • Geometric-transformation-based metamorphic testing approaches are developed for ADAS simulation testing. • Real-life bugs and issues are revealed in the LKASs of three famous ADAS simulation platforms: Simulink; CarMaker; and the open, web-based platform, 51Sim-One Cloud. • MT can enhance test standards and protocols, and alleviate the oracle problem in ADAS simulation testing.

On the road safety benefits of advanced driver assistance systems in different driving contexts

Advanced Driver Assistance Systems (ADAS) have introduced several benefits in the vehicular industry, and their proliferation presents potential opportunities to decrease road accidents. The reasons are mainly attributed to the enhanced perception of the driving environment and reduced human errors. However, as environmental and infrastructural conditions influence the performance of ADAS, the estimation of accident reductions varies across geographical regions. This study presents an interdisciplinary methodology that integrates the literature on advanced driving technologies and road safety to quantify the expected impact of ADAS on accident reduction across combinations of road types, lighting, and weather conditions. The paper investigates the safety effectiveness of ADAS and the distribution of frequency and severity of road accidents across 18 driving contexts and eight accident types. Using road safety reports from the United Kingdom (UK), it is found that a high concentration of accidents (77%) occurs within a small subset of contextual conditions (4 out of 18) and that the most severe accidents happen in dark conditions on rural roads or motorways. The results of the safety effectiveness analysis show that a full deployment of the six most common ADAS would reduce the road accident frequency in the UK by 23.8%, representing an annual decrease of 18,925 accidents. The results also show that the most frequent accident contexts, urban-clear-daylight and rural-clear-daylight, can be reduced by 29%, avoiding 7,020 and 3,472 accidents, respectively. Automatic Emergency Braking (AEB) is the most impactful technology, reducing three out of the four most frequent accident categories – intersection (by 28%), rear-end (by 27.7%), and pedestrian accidents (by 28.4%). This study helps prioritise resources in ADAS research and development focusing on the most relevant contexts to reduce the frequency and severity of road accidents. Furthermore, the identified contextual accident hotspots can assist road safety stakeholders in risk mitigation programs.

Validation Framework for Generic Radar Sensor Models

Automotive radar sensors are vital in Advanced Driver Assistance Systems (ADAS). To be more precise, their ability to explicitly measure the relative velocity to its targets is essential in Adaptive Cruise Control (ACC) and Emergency Braking (EB) applications. Nevertheless, ADAS are getting more and more complex, due to constantly increasing demands regarding safety and performance. As a result, to speed up the development and validation time of ADAS, part of the testing is performed in simulations. Replacing some of the test drives by the runs in virtual environments not only reduces the cost of a product, but also helps in fully safe execution of dangerous corner cases. However, to enable reliable testing of radar-based ADAS in virtual environments, high-fidelity radar sensor models are required. In order to prove the reliability of a given model, a proper evaluation process has to be conducted. This paper presents an end-to-end, straightforward methodology for performance assessment and fine-tuning of radar sensor models. To show how the full pipeline of the framework can be executed, an exemplary radar sensor model has been incorporated. The successful fine-tuning of the model proves the usefulness of the introduced method.

Object Detection, Localization and Tracking-Based Deep Learning for Smart Wheelchair

In this article, we present our work regarding the development of advanced driver-assistance systems for an electric-powered wheelchair. Our project aims at improving the autonomy of people with reduced mobility. After conducting a clinical study, we identified several use-cases. In this paper, we introduce the detection, localization and tracking of points of interest in the immediate surroundings of the chair in an indoor environment, i.e.: doors, handles, light switches, etc. The aim is not only to improve perception around the chair but also to enable semi-autonomous driving towards these targets. First, we introduce a repurposing of YOLOv3, the object detection algorithm, to our use case. Then, we show our use of the Intel Realsense camera for depth estimation. Finally, we describe our adaptation of the SORT algorithm to track 3D interest points. To validate our approach, we realized several experiments in a controlled indoor environment. The detection, distance estimation, and tracking pipeline is tested using our custom dataset. This includes corridors, doors, handles, and switches. One of the scenarios studied to validate the proposed platform includes not only the detection and tracking of objects but also the movement of the wheelchair towards one of these points of interest.

Driving Safety through ADAS: An Indian Perspective

Analysis of the National Motor Vehicle Crash Causation Survey, conducted by the National Highway Traffic Safety Administration (NHTSA), shows that driver error is a factor in 94% of crashes. Although it is important to remember multiple factors contribute to all crashes, the largest portion of driver error issues involve the driver failing to recognize hazards, including distraction. Around 3,700 people die in traffic every day around the world, and 100,000 are injured. The automotive industry is striving to make driving safer. ADAS in India is comparatively in a nascent stage. However, it is gradually gaining pace. The government's upcoming safety regulations and consumer awareness will give further impetus to this movement. So, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology. In addition to this, ADAS systems need to be very safe and robust, with the ability to perform in a variety of driving scenarios, and be very secure, being immune from any external cyber-attacks. In order to make ADAS systems safer, the AV will be required to drive more than a billion miles on real roads, taking tens and sometimes hundreds of years to drive those miles, considering even the most aggressive testing assumptions. Every small update to the AV will require another billion miles of testing to be approved for real world use. Moreover, the more advanced the technology becomes, the more miles will need to de driven. Real word testing plays a very crucial role in ADAS and AV development and testing. Nevertheless, relying only on real world testing will significantly slow down the development and testing of such technologies. This is where simulation comes into play. With the primary objective of road safety improvement, ADAS functionalities will definitely play a big role for automotive industry. In order to tackle Indian specific road infrastructure conditions, and thus improving the safety, a complete tool-chain for developing, deploying and validating ADAS functionalities need to be developed. The presented work shares insights of each and every aspect of this tool-chain with experimental results and real world correlations.

Analysis of advanced driver assistance systems in police vehicles: A survey study

Few studies have examined the impact of advanced driver-assistance systems (ADAS) on police officers to improve driver safety and prevent crashes. This is in spite of police officers having higher driving-related mortality rates than average civilians. To fill this gap, a survey study was conducted on 73 police officers to assess their opinions on various ADAS features as well as their recommendations for improvement. Results of the correlation analyses indicated that officer behavior and opinion on ADAS features were influenced by the trust officers had in the available ADAS systems among other key factors such as ADAS training and perceived usefulness. On this basis, guidelines for future research and development of ADAS were provided to improve officer driving safety in police operations. The guidelines need to be further validated in future driving simulation or naturalistic studies.

A Realistic Radar Ray Tracing Simulator for Large MIMO-Arrays in Automotive Environments

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations.

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.

Explaining Deep Learning-Based Driver Models

Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with “black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments.

Experimental research of the electromechanical steering system

The development of advanced driver-assistance systems (ADAS) is a global trend. One of the types of ADAS are systems that control the movement of a vehicle in a lane, preventing unintended departure from the lane. When implementing the lane keeping assist system, one of the most important functions is the implementation of external steering control. This article presents tests of the electromechanical steering system with the possibility of external control. Conducted experimental research of the developed system. Based on the test results, conclusions were made on the system’s compliance with the requirements of GOST 31507-2012, and recommendations were given on the applicability to vehicles of the GAZ Group, in particular, the lane keeping assist system.

Drivers’ Performance Examination using a Personalized Adaptive Curve Speed Warning: Driving Simulator Study

ABSTRACTA great number of traffic accidents occur during curve negotiation maneuvers. Most of these accidents could be avoided if drivers are provided with information that better guide them through the maneuver. Advanced Driver Assistance Systems (ADAS) such as Curve Speed Warning (CSW) have shown to be effective to improve safety on curve maneuvers by warning drivers of the speed required to make a safe curve maneuver. The effectiveness of such warning systems can potentially improve if the warnings are not only adapted to road and traffic condition but also adapted to individual drivers’ behavior. In this study, an Adaptive Curve Speed Warning (ACSW) system is developed that presents drivers a two-level visual and audio warning considering the variation in individual drivers’ perception-reaction time (PRT). The warning timing is adjusted according to a reward/punishment function to reinforce safer actions while providing an individualized in-time warning. Next, within a driving simulator environment, drivers’ performance using ACSW is compared to a CSW that does not consider PRT variation among drivers. Further, variation in drivers’ performance on curve maneuvers is discussed with respect to drivers’ approaching speed, variation in PRT, and braking behavior. Results show that drivers’ interaction with warning systems varies significantly based on their age and gender. In addition, results show how drivers approaching speed to a curve varies significantly based on road characteristics such as curve advisory speed and curve direction. Results from this study contribute to the development of more intelligent ADAS that could improve drivers’ comfort and safety.

Introducing Advanced Driver Assistance Systems

The introduction of Advanced Driver Assistance Systems (ADAS) in road traffic induces many complex questions. One of them is whether or not present legislation frameworks are able to accommodate a smooth development and market implementation of ADAS. This is strongly related to the aspect of traffic safety. The various aspects related to this issue are categorised based on an exploration of the functionality and possible failure of ADAS. Next, some problem categories are more in-depth elaborated. In particular attention is paid to the need for establishing safety requirements to the design and marketing of ADAS as well as the issue of liability regulation. It is concluded that decision making on safety requirements mainly takes place at an international level. However, so far hardly any requirements regarding ADAS have been laid down in compulsory rules yet. It is further concluded that current legal frameworks in both the fields of vehicle safety standards and liability provide for (some) flexibility towards technical developments regarding ADAS, i.e. these frameworks do not contain many ‘hard rules’ obstructing the introduction of ADAS. Concerning the safety regulation of ADAS it is argued that the speed of technological developments and the innovative and specific nature of ADAS technology generate various tensions. These tensions should have consequences for the weight that is put on public and private intervention mechanisms and the relation between preventive safety standards and reactive regimes such as product liability and post-market controls.

Introducing Advanced Driver Assistance Systems: Some Legal Issues

The introduction of Advanced Driver Assistance Systems (ADAS) in road traffic induces many complex questions. One of them is whether or not present legislation frameworks are able to accommodate a smooth development and market implementation of ADAS. This is strongly related to the aspect of traffic safety. The various aspects related to this issue are categorised based on an exploration of the functionality and possible failure of ADAS. Next, some problem categories are more in-depth elaborated. In particular attention is paid to the need for establishing safety requirements to the design and marketing of ADAS as well as the issue of liability regulation. It is concluded that decision making on safety requirements mainly takes place at an international level. However, so far hardly any requirements regarding ADAS have been laid down in compulsory rules yet. It is further concluded that current legal frameworks in both the fields of vehicle safety standards and liability provide for (some) flexibility towards technical developments regarding ADAS, i.e. these frameworks do not contain many ‘hard rules’ obstructing the introduction of ADAS. Concerning the safety regulation of ADAS it is argued that the speed of technological developments and the innovative and specific nature of ADAS technology generate various tensions. These tensions should have consequences for the weight that is put on public and private intervention mechanisms and the relation between preventive safety standards and reactive regimes such as product liability and post-market controls.