Advanced Driver Assistance Systems Systems Research Articles

Predictive Models of Brake Reaction and Steering of Police Officers in Critical Driving Situations

Motor vehicle crashes involving police vehicles significantly impact law enforcement officer (LEO) safety, often occurring during high-risk maneuvers like pursuit driving and sudden lane changes. These incidents are exacerbated by LEOs’ interaction with in-vehicle technologies, including Mobile Computer Terminals. Advanced Driver-Assistance Systems (ADAS) could mitigate these risks, though existing ADAS algorithms are not tailored to the unique work demands. To address these challenges, our study develops predictive models for LEO braking and steering in critical situations, based on a driving simulator experiment with police officers. Both braking and steering models were constructed and identified as effective in predicting police officer braking response and steering. The models can be practically applied as part of ADAS systems to deliver warnings at appropriate times, adapting to police officer demands in critical driving situations. Study implications contribute to the wider body of knowledge on surface transportation, occupational safety, and the use of adaptive models.

Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection

The recent advancements in Advanced Driver Assistance Systems (ADAS) have significantly contributed to road safety and driving comfort. An integral aspect of these systems is the detection of driver anomalies such as drowsiness, distraction, and impairment, which are crucial for preventing accidents. Building upon previous studies that utilized ensemble model learning (XGBoost) with deep learning models (ResNet50, DenseNet201, and InceptionV3) for anomaly detection, this study introduces a comprehensive feature importance analysis using the SHAP (SHapley Additive exPlanations) technique. The technique is implemented through explainable artificial intelligence (XAI). The primary objective is to unravel the complex decision-making process of the ensemble model, which has previously demonstrated near-perfect performance metrics in classifying driver behaviors using in-vehicle cameras. By applying SHAP, the study aims to identify and quantify the contribution of each feature – such as facial expressions, head position, yawning, and sleeping – in predicting driver states. This analysis offers insights into the model’s inner workings and guides the enhancement of feature engineering for more precise and reliable anomaly detection. The findings of this study are expected to impact the development of future ADAS technologies significantly. By pinpointing the most influential features and understanding their dynamics, a model can be optimized for various driving scenarios, ensuring that ADAS systems are robust, accurate, and tailored to real-world conditions. Ultimately, this study contributes to the overarching goal of enhancing road safety through technologically advanced, data-driven approaches.

Current Approaches in Traffic Lane Detection: a minireview

The continuous development and importance of the field of road transport these days make it necessary to design, develop and implement technological solutions that reduce (eliminate as much as possible) the risk of road accidents. Such a technological solution is also represented by advanced driver assistance systems (ADAS), systems that assist drivers in various ways, such as collision avoidance, automatic parking, adaptive cruise control, attention and lane departure warnings. Over the next ten years, there will likely be a rise in the need for ADAS system deployment in automobile construction, driven by consumer and regulatory interest in safety applications that protect drivers and lower accident rates. At the moment, autonomous emergency braking and forward collision warning systems are mandated for all cars in the US and the EU. Additionally, advanced driver assistance systems (ADAS) may soon distinguish automobile brands and have a significant impact on consumer preference. The present work aims to provide a general picture related to the current research and development of ADAS systems that refer to the detection of the traffic lane and lane markings. The approaches are presented regarding: the current development directions of ADAS systems, current traffic lane detection techniques, traffic lane detection methods and the use of artificial intelligence techniques in this field. The general conclusion is that further research is needed in the field, research to increase the performance of traffic lane detective systems by using advanced algorithms and easy-to-implement methods that do not require large hardware resources.

Testing driver warning systems for off-road industrial vehicles using a cyber-physical simulator

ADAS (Advanced Driver Assistance Systems) are becoming increasingly popular in on-road vehicles due to their safety, productivity, and cost savings. In the same way, off-road vehicles can benefit from ADAS systems to improve the security of drivers and workers in industrial settings. In this work, we study, design, and develop a novel security system to be integrated into industrial vehicles. This system is built to provide one-way Human Computer Interaction, from the computer to the human, so providing, through the interaction with the ADAS system, feedback to drivers about their surroundings, such as nearby workers, and thus helping to avoid collisions and prevent incidents. The study evaluates the quality of different feedback mechanisms, with the goal of designing the ADAS that produces the best User eXperience (UX). These feedback mechanisms are generated by LEDs in different display formats and colors, as well as with haptic feedback. We created a hybrid testbed using a realistic ADAS and a forklift simulator, integrating the system into a physical structure that resembles an industrial vehicle (a forklift) and used a computer-based simulation of a warehouse to gather the information from users. We performed a study with 36 people for the evaluation of the different feedback mechanisms tested, evaluating the results both from an objective point of view based on the results of the simulation, and a subjective point of view through a questionnaire and the stress of the users in each test.

An injury risk-based comprehensive framework for testing and assessing ADAS functions in critical road scenarios

Assisted driving is currently considered a key aspect for improving road safety, and automakers and OEMs are working to achieve higher levels of vehicle automation by introducing new technologies and Advanced Driver Assistance Systems (ADAS) in the circulating fleet. This trend requires test protocols for vehicle safety assessment to be frequently reviewed and updated, considering the latest advances in the state of the art regarding ADAS functions and systems. As of today, performance assessment programs (such as NCAP) mainly evaluate how an ADAS behaves in terms of crash avoidance in specific critical scenarios, which represent the most frequent crash constellations among real-world impacts. However, enhanced safety can be also obtained in case the impact is not avoided if a decrease in Injury Risk (IR) for the involved road users is achieved by ADAS intervention, compared to the case of no intervention.The purpose of this work is to propose an overall framework to draft or update test protocols for ADAS performance assessment based on real car-to-car impact observations, representative of impact scenarios in terms of both occurrence frequency and IR. First, the in-depth accident database IGLAD is analyzed to identify the most relevant car-to-car accident scenarios based on a relevance indicator, i.e., the risk level being the multiplication of the occurrence frequency and IR for a specific scenario. For each relevant scenario, a risk level-based strategy to identify one significant closing speed between vehicles for the tests is defined; the test collision speed for the two vehicles is determined analogously, and the risk level for each combination of speeds in a scenario represents the maximum achievable score by the ADAS if the collision is averted. Considering the well-established Euro NCAP framework as a relevant starting point for the definition of test protocols, two examples are highlighted regarding the proposal of a new test protocol and an update of an already existing one. Finally, a method is proposed for ADAS performance assessment if the impact is not avoided, scaling the maximum achievable score based on the IR reduction consequent to the ADAS intervention.

Retrofitting pedestrian and cyclist collision warning for improved safety of heavy trucks employed in urban areas: a case study in Florence and Pistoia, Italy

The increasing use of heavy vehicles for services such as public transport, waste collection and logistics in densely populated urban areas causes numerous collisions with serious consequences every year, particularly for pedestrians, cyclists and motorcyclists. Advanced Driver Assistance Systems (ADAS) have proven to be a solution because they can significantly reduce the occurrence and consequences of road crashes. Although many ADAS technologies will be mandatory on new trucks in 2024, it will take more than a decade before the benefits are realised due to the average fleet renewal time. To accelerate this process, the most promising solution is therefore the introduction of aftermarket ADAS (also referred to as ‘retrofit’ ADAS) on the circulating fleet. This study aims to identify a tailored solution for retrofitting ADAS capable of providing pedestrian collision warnings on heavy vehicle fleets currently circulating in urban areas. The study involves a sample of vehicles and drivers operating in the metropolitan areas of Florence and Pistoia for waste collection services. The company owner of the vehicles integrated a commercial camera-based ADAS system with three additional cameras installed on the frontal bumper and edges of the vehicle, linked with three screens positioned in the vehicle cabin. In this study, a field test campaign was performed integrating trials in a proving ground closed to traffic with testing and monitoring in urban areas. This allowed to define a set of recommended parameters depending on vehicle speed that represent a good balance between alert frequency, minimizing false positives and ensuring that the alerts indicate real potential hazards to the driver. The recommendations provided in this study can also be extended to other vehicle fleets so that rapid and substantial improvements in the safety of heavy vehicles operating in cities can be achieved.

Minimizing optical attribute errors for a lane departure warning system using an ultra-wide-angle camera

Advanced driver assistance systems (ADAS) rely on lane departure warning (LDW) technology to enhance safety while driving. However, the current LDW method is limited to cameras with standard angles of view, such as mono cameras and black boxes. In recent times, more cameras with ultra-wide-angle lenses are being used to save money and improve accuracy. However, this has led to some challenges such as fixing optical distortion, making the camera process images faster, and ensuring its performance. To effectively implement LDW, we developed three technologies: (i) distortion correction using error functions based on the projection characteristics of optical lenses, (ii) automatic vanishing point estimation using geometric characteristics, and (iii) lane tracking and lane departure detection using constraints. The proposed technology improves system stability and convenience through automatic calculation and updating of parameters required for LDW function operation. By performing automatic distortion correction and vanishing point estimation, it has also been proven that fusion with other ADAS systems including front cameras is possible. Existing systems that use vanishing point information do not consider lens distortion and have slow and inaccurate vanishing point estimation, leading to a deterioration of system performance. The proposed method enables fast and accurate vanishing point estimation, allowing for adaptive responses to changes in the road environment.

Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers’ mental models, defined as an individual’s cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants’ performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies.

"Cyclist at 12 o'clock!": a systematic review of in-vehicle advanced driver assistance systems (ADAS) for preventing car-rider crashes.

While Advanced Driver Assistance Systems (ADAS) have become a prominent topic in road safety research, there has been relatively little discussion about their effectiveness in preventing car collisions involving specific vulnerable road users, such as cyclists. Therefore, the primary objective of this systematic literature review is to analyze the available evidence regarding the effectiveness of in-vehicle ADAS in preventing vehicle collisions with cyclists. To achieve this goal, this systematic review analyzed a selection of original research papers that examined the effectiveness of ADAS systems in preventing car-cyclist collisions. The review followed the PRISMA protocol, which led to the extraction of 21 eligible studies from an initial pool of 289 sources indexed in the primary scientific literature databases. Additionally, word community-based content analyses were used to examine the research topics and their links within the current scientific literature on the matter. Although the current number of studies available is still scarce (most sources focus on car-motorcyclist or car-pedestrian crashes), the overall quality of the available studies has been reasonably good, as determined by the selected evaluation methods. In terms of studies' outcomes, the literature supports the value of in-vehicle ADAS for preventing car-cyclist crashes. However, threatful side effects such as unrealistic expectations of these systems and users' overconfidence or desensitization are also highlighted, as well as the need to increase driver training and road user awareness. The results of this study suggest that Advanced Driver Assistance Systems have significant potential to contribute to the prevention of driving crashes involving cyclists. However, the literature emphasizes the importance of concurrently enhancing user-related skills in both ADAS use and road-user interaction through educational and training initiatives. Future research should also address emerging issues, such as ADAS-related behavioral ergonomics, and conduct long-term effectiveness assessments of ADAS in preventing car-cycling crashes and their subsequent injuries. PROSPERO, unique identifier CRD42024505492, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=505492.

Estimating the crash reducing effect of Advanced Driver Assistance Systems (ADAS) for vulnerable road users

Vulnerable road users (VRUs) are frequently involved in road traffic crashes worldwide, and the most serious consequences often occur in collisions with motor vehicles. Countermeasures such as advanced driver assistance systems (ADASs) for have been developed and implemented in vehicles to reduce these crashes. Estimations of the effectiveness of these systems are so far dominated by prospective studies; only a few analyze real-world crash data. The objective of this study was to estimate the crash-reducing effects of the VRU ADAS systems in car-to-pedestrian and car-to-bicycle crashes involving Volvo cars in Sweden using real-world crash data. Car-to-pedestrian and car-to-bicycle collisions across Sweden from 2015–2020 (in which the car was moving forward) were identified using insurance claims at If P&C insurance. Volvo car models with ADAS VRU and comparable Volvo models without the system were selected. Poisson regression was used to estimate the effect of the VRU ADAS on the crash rate, calculated per insured vehicle-years. For the second generation of VRU ADAS, the straight crossing path (SCP) and parallel (Longitudinal) crash rate was found to be 12% less in car-to-pedestrian/bicycle crashes—23% less in car-to-pedestrian crashes and 6% less in car-to-bicycle crashes. However, no results were statistically significant. There was no difference in crash rates when evaluating all crash situations where the car was moving forward. The limitation of this study is the low number of crashes. Reducing crashes and injuries in vehicle-to-VRU conflicts is of utmost importance for fulfilling the aims highlighted in Agenda 2030. Improving ADAS to better avoid collisions with pedestrians and cyclists has the potential to greatly improve traffic safety; future generations of the system are expected to increase VRU safety benefits. Measuring ADAS effects in real-world traffic and interpreting and relating the results are essential to ensure that future systems improve VRU safety.

Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis.

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. 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. 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. 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.

Exploring the factors influencing acquisition and learning experiences of cars fitted with advanced driver assistance systems (ADAS)

Advanced Driver Assistance Systems (ADAS) have shown substantial potential to increase road safety. To guarantee a growing presence and correct use of ADAS, road safety initiatives such as driver education and programs to promote safer vehicles need to be oriented towards the specific needs of individual drivers. The aim of this study was to explore a sample of Australian drivers’ rationale for driving a vehicle with ADAS and the strategies that they use when learning to operate the functions of ADAS. Semi-structured interviews were conducted with 48 Australian drivers aged between 19 and 78 years (M = 41.54, SD = 16.32; 25 females). The results were organised into four themes: (i) reasons for driving/purchasing a car with ADAS, (ii) systems influencing driver purchasing systems, (iii) strategies used by drivers when learning about ADAS, and (iv) perceptions regarding the importance of learning and training to use ADAS. Acquisition of vehicles with ADAS frequently occurred because of the perceived safety benefits of specific systems (e.g., rear parking sensor or in-vehicle information system). Additionally, some participants did not highlight the safety benefits as the main reason for driving/purchasing a car with ADAS. Indeed, some participants mentioned that ADAS acquisition was not a reasoned process but rather an opportunistic decision when upgrading their vehicles, mainly because these systems were already included in a vehicle. Regarding education, it was found that participants were using trial-and-error as their main approach to learning about the capabilities of ADAS and official sources of knowledge about the system were rarely consulted. On a positive note, participants recognised the importance of learning to use ADAS systems correctly, showing that there is demand for effective and engaging driver education.

Smart Tramway Systems for Smart Cities: A Deep Learning Application in ADAS Systems

Artificial intelligence and deep learning-based techniques undoubtedly are the future of Advanced Driver-Assistance Systems (ADAS) technologies. In this article is presented a technique for detecting, recognizing and tracking pedestrians, vehicles and cyclists along a tramway infrastructure in a complex urban environment by Computer Vision, Deep Learning approaches and YOLOv3 algorithm. Experiments have been conducted in the tramway Line 2 “Borgonuovo –Notarbartolo” (Palermo, Italy) in correspondence of the tramway segments crossing a roundabout having an external diameter of 24 m. A survey vehicle equipped with a video camera was used in the study. The results of the research show that the proposed method is able to search and detect the position and the speed of road users near and over the rails in front of the tram in a very precise way as demonstrate by the estimated values of the Accuracy, Loss and Precision obtained during the neural networks training process. The implementation of this advanced detection method in ADAS systems may increase the safety of novel autonomous trams and autonomous rapid trams (ARTs).

An optimized AdaBoost Multi-class support vector machine for driver behavior monitoring in the advanced driver assistance systems

Advanced Driver Assistance System (ADAS) is a Cyber-Physical System (CPS) application mainly developed for human–machine interaction. We employ the CPS information on ADAS while driving to not only improve the car’s driving conditionbut also alter the driving concepts to improve the functionality and safety of the vehicle. The driver can review the vehicle's feedback information in order to improve the ADAS system's ability to help in driving and completing the process of human–computer interaction. The data obtained during this interaction process are mainly high dimensional and consist of noisy/irrelevant features that pose an important obstacle to our ability to better grasp the car's driving states. In this study, we proposed an AdaBoost Multi-class Support Vector Machine (MSVM) with Cat Mouse Optimizer (CMO) algorithm for ADAS intrusion detection. The evolutionary algorithm is used in this research to tackle the drawbacks related with MSVM such as kernel, poor performance, minimum accuracy, overfitting issues, stability, and so on. Metaheuristics algorithms are an excellent and trustworthy solution for modeling MSVM parameters and producing optimal outcomes. As a result, we used the CMO method to optimize the MSVM parameters.From this, both normal and abnormal behaviors are predicted that affect the driving system.The experimental data is acquired from the power supply voltage data generated by the Jiangxi bus company. Data pre-processing is the major step to clean up, normalize and reshape the data for further processing. The low dimensionality features are extracted and finally, AdaBoostMSVM with CMO algorithm classifies both normal and anomalyactivities of driving vehicles. The results show that, in the majority of cases, the proposed model provides more accurate predictions in terms of accuracy, recall, precision, F-score, post hoc Nemenyi test, and Friedman test.Using performance metrics, the proposed method achieved 91.45 % accuracy, 94.45 % F-score, 93.89 % precision, and 94.90 % recall, outperforming other existing methods.

Drowsiness Detection System Based on Machine Learning Using Eye State

Drowsiness is one of the major causes of driver-induced traffic accidents. The interactive systems developed to reduce road accidents by alerting drivers is called as Advanced Driver Assistance Systems (ADAS). The most important ADAS are Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In this study, an ADAS system based on eye state detection is presented to detect driver drowsiness. First, Viola-Jones algorithm approach is used to detect the face and eye areas in the proposed method. The detected eye region is classified as closed or open by making use of a machine learning method. Finally, the eye conditions are analyzed at time domain with PERcentage of eyelid CLOsure (PERCLOS) metric and drowsiness conditions are determined by Support Vector Machine (SVM), kNN and decision tree classifiers. The proposed methods tested on 7 real people and drowsiness states are detected at 99.77%, 94.35%, and 96.62% accuracy, respectively.

Studying the effects of an advanced driver-assistance system to improve safety of cyclists overtaking

Among all crashes involving cyclists, a motorist approaching from behind a cyclist on a shared lane is particularly dangerous and likely to result in serious injuries and fatalities. Previous research has highlighted that inadequate lateral distance and high vehicle speed are among the main contributing factors of crashes involving cars overtaking cyclists.A new advanced driver assistance system (ADAS) which supports drivers as they overtake cyclists was designed to avoid or, at least, mitigate crashes. In human–machine interface (HMI) design, the information was presented via multiple modalities with a multistage warning system. A combination of lateral clearance (LC) and time-to-danger (TTD) parameters was used as ADAS activation criterion. Experimentation was carried out using the medium-fidelity driving simulator at the Transportation Research Institute (IMOB) of Hasselt University in Belgium.Forty-eight drivers drove the two-lane rural experimental route two times, in baseline condition and with the ADAS activated, testing three overtaking events. Statistical tests showed that the proposed in-vehicle driving assistance system had a significant effect in increasing 1) the length of the passing phase, 2) the LC in the overtaking passing phase, and 3) the TTD along the overtaking maneuver. No effect of the ADAS system on vehicle speed was observed. Overall, the designed system is effective in improving car-cyclist overtaking behaviour in terms of both safety and cyclists’ mobility.

ObjectDetect: A Real-Time Object Detection Framework for Advanced Driver Assistant Systems Using YOLOv5

In the recent times, there has been a lot of speculation related to advanced driver-assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as R-CNN, Fast R-CNN, and YOLO were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called “A Real-Time Object Detection Framework for Advanced Driver Assistant Systems” by implementing the state-of-the-art object detection algorithm YOLOv5 which improves the speed in detection of object over real-time. This paper provides a comparison between other state-of-the-art object detectors such as YOLOv3 and YOLOv4. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLOv5 proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called “ObjectDetect” which helps users make better decisions on the road. “ObjectDetect” consists of a simple user interface that displays alerts and warnings.

Development of an Emergency Stop Assist System with Pull-Over Function

<div class="section abstract"><div class="htmlview paragraph">Although accidents caused by drivers’ physical condition account for only a small percentage of all traffic accidents, they are highly likely to lead to serious accidents if they do occur, so speedy actions from both product and social rule aspects are desirable. In response, many companies have developed and commercialized emergency stop assist system which stops in the lane, and also the development and discussion of emergency stop assist systems with pull-over function is being promoted in several countries in order to ensure the safety of the own vehicle, the following vehicle, and the evacuation of passengers, and to shorten the rescue time. Regarding Advanced Driver Assistance Systems (ADAS) in the market, the ones which are categorized as Lv.2 by SAE are mainstream, so the development of emergency stop assist systems with pull-over function that utilize Lv.2 ADAS is required in order to spread the use of it. In this paper, we describe the basic design and concept of an emergency stop assist system using Lv.2 ADAS which is based on the premise that the driver must be appropriately involved to ensure safety. This paper also describes an approach to increase the acceptability of the emergency stop assist system by switching between pull-over and stop-in-the-lane according to the situation, and also discusses the technology for judging whether the driver is abnormal or not in the Lv.2 ADAS system in a system that allows hands-off operation under certain circumstances, and the notification to surrounding vehicles in consideration of regional characteristics.</div></div>

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics.

Modern Advanced Driver-Assistance Systems (ADAS) combine critical real-time and non-critical best-effort tasks and messages onto an integrated multi-core multi-SoC hardware platform. The real-time safety-critical software tasks have complex interdependencies in the form of end-to-end latency chains featuring, e.g., sensing, processing/sensor fusion, and actuating. The underlying real-time operating systems running on top of the multi-core platform use static cyclic scheduling for the software tasks, while the communication backbone is either realized through PCIe or Time-Sensitive Networking (TSN). In this paper, we address the problem of configuring ADAS platforms for automotive applications, which means deciding the mapping of tasks to processing cores and the scheduling of tasks and messages. Time-critical messages are transmitted in a scheduled manner via the timed-gate mechanism described in IEEE 802.1Qbv according to the pre-computed Gate Control List (GCL) schedule. We study the computation of the assignment of tasks to the available platform CPUs/cores, the static schedule tables for the real-time tasks, as well as the GCLs, such that task and message deadlines, as well as end-to-end task chain latencies, are satisfied. This is an intractable combinatorial optimization problem. As the ADAS platforms and applications become increasingly complex, such problems cannot be optimally solved and require problem-specific heuristics or metaheuristics to determine good quality feasible solutions in a reasonable time. We propose two metaheuristic solutions, a Genetic Algorithm (GA) and one based on Simulated Annealing (SA), both creating static schedule tables for tasks by simulating Earliest Deadline First (EDF) dispatching with different task deadlines and offsets. Furthermore, we use a List Scheduling-based heuristic to create the GCLs in platforms featuring a TSN backbone. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems. The results show that our heuristic strategy can find correct solutions that meet the complex timing and dependency constraints at a higher rate than the related work approaches, i.e., the jitter constraints are satisfied in over 6 times more cases, and the task chain constraints are satisfied in 41% more cases on average. Our method scales well with the growing trend of ADAS platforms.

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.