OSEK OS 기반 ACC/LKS 통합제어기 구현

<div class="section abstract"><div class="htmlview paragraph">New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework’s elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS’ components, and different types of input attributes to these MLA are proposed for some basic ADAS’ components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-Learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS’ components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.</div></div>

The automotive industry is more concerned about vehicle and road safety. Nowadays the Govt. has introduced new safety regulations to improve the vehicle and road safety. Most of the accidents are happening because of human errors, violation of traffic rules, etc. To avoid or reduce these accidents and to meet the Govt. safety regulations the automotive industries started implementing new technologies like Advanced Driver Assistance System (ADAS), Passive and Active safety system in vehicles. ADAS is the most important safety feature in modern automobiles. The key functionality of ADAS is to provide more comfort to the driver and to improve road and vehicle safety by minimizing human errors. To achieve these functionalities the system use sensors like Radar (Radio Detection and Ranging), Camera, Light Detection and Ranging (LIDAR), and Ultrasonic sensors. Based on sensor input Electronic Control Unit (ECU) delivers the information to driver in terms of warnings, alarms, etc. or system takes the longitudinal and lateral control to bring the vehicle to safer zone. Focus of this paper is to discuss the hardware and software technologies used for the development of ADAS systems and its challenges.

Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems

Advanced Driver Assistance Systems (ADAS) have emerged as a critical technology in enhancing vehicle safety, providing convenience to drivers, and facilitating the transition toward fully autonomous vehicles. The core of these systems relies heavily on computer vision (CV) technologies, which enable vehicles to interpret and understand their surrounding environment in real time. The integration of computer vision in ADAS allows vehicles to recognize and process various objects such as pedestrians, vehicles, traffic signs, and lane markings, significantly improving both safety and the driving experience. In this paper, we explore the pivotal role of computer vision in the development and implementation of ADAS in autonomous vehicles. We begin by reviewing the evolution of ADAS, detailing the different sub-systems such as lane departure warning, automatic emergency braking, adaptive cruise control, and parking assistance, all of which depend on sophisticated image processing techniques. Furthermore, the paper examines the intersection of computer vision with other emerging technologies such as machine learning, deep learning, and sensor fusion. We discuss how these innovations enable ADAS to function more effectively in complex, dynamic environments. The methodology section focuses on cutting-edge algorithms used for object detection, semantic segmentation, and real-time image processing, which are integral to the functionality of modern ADAS. Additionally, we evaluate the challenges that computer vision systems face in real-world applications, including issues with sensor calibration, environmental conditions, computational limitations, and the integration of data from multiple sensors (e.g., cameras, LiDAR, radar). The paper also presents an analysis of the results obtained from various real-world implementations of ADAS technologies, highlighting improvements in driver safety and the reduction of traffic-related accidents. We explore the potential of computer vision in enabling fully autonomous driving by addressing the current gaps in technology and the advancements needed to close these gaps. Finally, the paper concludes with a discussion on the future of ADAS and the role of computer vision in realizing fully autonomous, reliable, and efficient transportation systems. We argue that continued research and development in computer vision, combined with innovations in deep learning and sensor technologies, are critical to advancing the capabilities and reliability of autonomous vehicles in the near future.

Aim: With cutting-edge technologies revolutionizing modern life, autonomous vehicles connected with in-frastructure applications are significantly impacting human life through technologies such as Vehi-cle-to-Everything (V2X), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Vehicle (V2V). This paper pre-sents the state of the art in fuzzy models implementing adaptive cruise control (ACC) for vehicles and com-pares their respective results. Methods: The demand for efficient automobile cruise control systems is on the rise. However, these systems are very complex and require an accurate model, which is difficult to achieve due to complex and variable road conditions. Therefore, reaching level 5 autonomous driving, which refers to a fully automated vehicle capable of performing all driving functions, will eventually require fuzzy logic inference to help incorporate human-like thinking into the vehicle. Results: This paper investigates both current and previous research, comparing the overall performance of various fuzzy systems to non-fuzzy systems. Conclusion: This paper presents various fuzzy logic cruise controllers aimed at improving and enhancing the autonomous driving experience. Among the methods reviewed are Fuzzy PID controllers, Fuzzy MPC with Type 1 and Type 2 using genetic algorithms, and fuzzy cooperative adaptive cruise control. The challenging problem of comparing different algorithms for fuzzy cruise control has been considered, studied, and ana-lyzed. The main conclusion of this comparative study is that applying different methods to the same adaptive cruise control system results in varying responses and output characteristics.

This paper discusses the market trends and advantages of a safety system integrating LKS (Lane Keeping System) and ACC (Adaptive Cruise Control), referred to as the LKS+ACC system, and proposes a method utilizing the range data from ACC for the sake of lane detection. The overall structure of lane detection is the same as the conventional method using monocular vision: EDF (Edge Distribution Function)-based initialization, sub-ROI (Region Of Interest) for left/right and distance-based layers, steerable filter-based feature extraction, and model fitting in each sub-ROI. The proposed method adds only the system for confining lane detection ROI to free space that is established by range data. Experimental results indicate that such a simple adaptive ROI can overcome occlusion of lane markings and disturbance of neighboring vehicles.

In control system of vehicle chassis, two integrated control sub-systems of chassis have achieved some better results than a single sub-system control. However the two integrated sub-system control can not improve some dynamic performance on vehicle when other sub-system of chassis is disturbed. In order to improve vehicle dynamic performance of some sub-system, an integrated control method based on multi-system with suspension, steering system and brake system is designed. In the model, a 14-DOF vehicle model is used, and an integrated control method based on multi-system of chassis is designed in software of Matlab/Simulink with an integrated controller of PID. Simulation results show that the overall vehicle performance based on the three integrated control systems of chassis is better than those of two integrated control system.

• The performance of Adaptive Cruise Control (ACC) is limited on road curves. • The Intelligent Speed Adaptation for Visibility (V-ISA) adjusts speed based on road conditions. • A driving simulation study assessed the influence of ACC and V-ISA on driver behaviour. • Integrating ACC and V-ISA significantly improved longitudinal driving performance. • The system achieved high system acceptance with no added mental workload. Advanced Driver Assistance Systems (ADAS) have become an integral part of modern vehicles, significantly changing the driving experience. Adaptive Cruise Control (ACC) assists drivers in maintaining a constant speed and a safe following distance; however, it does not moderate the speed along curves when sight limitations occur, thus compromising safety. To address these challenges, this study integrates the Intelligent Speed Adaptation for Visibility (V-ISA) system with ACC. The V-ISA system dynamically adjusts the vehicle speed based on real-time visibility conditions, thereby enhancing safety on road curves where sight distance is restricted. Additionally, this study evaluates the impact of the ACC + V-ISA system on driver performance, specifically focusing on speed management, lateral vehicle control, and driver experience with assistance systems. The study was conducted in a simulated environment with forty-five participants. The results revealed that the ACC + V-ISA system effectively reduced vehicle speed on curves with limited visibility. The ability of the system to regulate speed according to prevailing road conditions highlights its potential to improve road safety. Moreover, the findings suggest that using the V-ISA system integrated with ACC maintains user acceptance and satisfaction while imposing no additional mental workload compared to using ACC alone. These insights are crucial for the future design of ADAS and autonomous vehicles and emphasize the importance of introducing this system into vehicles. LED bar colour

Much of the existing research on drivers’ understanding of adaptive cruise control (ACC), a type of advanced driver assistance system, was conducted several years ago. Through an online survey, this study aimed to assess ACC knowledge among ACC owners and non-owners now that this system is more widely available. Along with knowledge of ACC features and limitations, demographic information, experience with technology, and experience with ACC (for owners) were also collected to investigate which factors predicted knowledge of ACC features and limitations. Results showed that owners today may have a better understanding of some of the main limitations of ACC compared with research conducted over 10 years ago. However, a large percentage of owners still had misperceptions about their ACC system. While owners had a slightly higher percentage of correct answers overall, they did not differ from non-owners in their knowledge of limitations. As this technology is becoming more common, even non-owners may be becoming aware of common limitations; owning and using ACC does not seem to result in a better system understanding. Higher income was associated with a higher percentage of correct responses on the ACC knowledge questionnaire for both owners and non-owners, and for non-owners, higher education level was also significantly associated with a higher percentage of correct responses. Future research should focus on developing training materials that are accessible to all drivers, so that drivers in lower education and income groups are also supported to understand how advanced driver assistance systems work and benefit from these technologies.

In the Netherlands, a field operational test was conducted in 2006 to assess the impact of two advanced driver assistance systems (ADAS), namely adaptive cruise control (ACC) and lane departure warning (LDW) systems. The research goal was to estimate the effects of these systems on road capacity, safety and emissions. In this paper we focus on the interaction between driver and ACC system using the data from this FOT. It is found that drivers choose headway settings according to their manual driving behavior. Moreover, they often keep the system deactivated under dense traffic conditions. It is also observed from the data that the system, once de-activated, either automatically or manually, needs some time to become again active. These findings imply that, even with 100% cars on the road equipped with ACC, manual driving behavior will still be a determinant factor.

Objective: As one of the bases for designing a humanlike brake control system for the intelligent vehicle, drivers’ deceleration behavior needs to be understood. There are two modes for drivers’ deceleration behavior: (i) brake pedal input, by applying brake system to reduce the speed; (ii) no pedal input, by releasing the accelerator pedal without pressing the brake pedal, thus decelerating by naturalistic driving resistance. The deceleration behavior that drivers choose to press the brake pedal has been investigated in previous studies. However, releasing the accelerator pedal behavior has not received much attention. The objective of this study is to investigate factors that influence drivers’ choice of the two deceleration modes using naturalistic driving data, which provide a theoretical foundation for the design of the brake control system.Methods: A logistic model was constructed to model drivers’ deceleration mode, valued as “no pedal input” or “brake pedal input” for dependent variables. Factors such as Light condition, Intersection mode, Road alignment, Traffic flow, Traffic light, Ego-vehicle motion state, Lead vehicle motion state, Time headway (THW), and Ego-vehicle speed were considered in the model as independent variables.Results: 393 deceleration events were selected from the naturalistic driving data, which used as the database for the regression model. As a result, 6 remarkable factors were found to influence drivers’ deceleration model, which include Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. Specifically, (1) the possibility of drivers choosing “no pedal input” is gradually increasing with the increase of THW and speed; (2) The drivers prefer to choose “no pedal input” when the lead vehicle is decelerating compared to it’s stationary. This probability is relatively high when the lead vehicle is traveling along the road; (3) the possibility of choosing “no pedal input” at intersection is higher than roads without intersection; (4) the possibility of choosing “no pedal input” is higher when traveling with more traffic flow.Conclusion: The drivers’ deceleration behavior can be divided into “no pedal input” and “brake pedal input.” The following six factors significantly affect drivers’ choice of deceleration mode: Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. The logistic regression model can quantify the influence of these six factors on drivers’ deceleration behavior. This study provides a theoretical basis for the braking system design of ADAS (Advanced Driving Assistant System) and intelligent control system.

<div class="section abstract"><div class="htmlview paragraph">Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system. And the input penalty factor of the adaptive cruise control system is designed as a variable parameter for the collaborative control model. On this basis, the longitudinal speed, reciprocal of speed and penalty factor are used as advance variables to design fuzzy rules of the system. And the nonlinear Takagi-Sugeno fuzzy model is established by fuzzifying the local linear model. Then, the vehicle following cruise controller considering the lateral stability is designed by parallel distribution compensation method. Finally, the TruckSim/Simulink co-simulation model was built for testing. The test results show that the proposed controller can improve the lateral stability of the vehicle during the following process, reduce the risk of instability of the vehicle, and improve the overall safety of the automatic driving system.</div></div>

Abstract: The development of modern driver assistance systems, such as Adaptive Cruise Control (ACC) and its enhanced version ACC+, represents a significant step towards improving road safety. This article analyzes the operation of these systems in the context of the causes and effects of traffic accidents. Based on the reconstruction of a specific traffic incident, a detailed assessment of the impact of ACC and ACC+ on traffic safety was conducted. The study is based on the analysis of measurable technical parameters that change under varying road conditions, both with the systems activated and deactivated. The paper also discusses examples of preventive strategies for similar incidents, utilizing new technologies applied in modern vehicles. Particular attention is given to differences in vehicle behavior under various operational scenarios of ACC and ACC+ systems, with a focus on their impact on the safety of road users. This article contributes to the development of knowledge on minimizing accident risks through advanced driver assistance systems. Keywords: ACC; ACC+; Safety; Traffic

EPS systems integrate Advanced Driver Assistance Systems (ADAS) while the driver controls the steering wheel. Touch sensors, torque, and position sensors improve driver involvement, safety, and control in EPS. ADAS features like lane-keeping assistance and adaptive cruise control are activated by the driver’s steering wheel touch. EPS with driver-enabled ADAS integration, using touch sensors, empowers drivers while leveraging enhanced automation. The vehicle model design incorporates touch sensors, Arduino, and motor drivers to enable Advanced Driver Assistance Systems (ADAS) exclusively when the driver holds the steering wheel. The touch sensors detect the driver’s touch on the steering wheel, triggering the ADAS functionalities and allowing the vehicle to move. The Arduino microcontroller, equipped with embedded C code, processes the touch sensor inputs and communicates with the motor drivers to initiate vehicle propulsion. The embedded C code facilitates real-time monitoring and control of the touch sensor inputs, enabling a seamless integration between driver interaction and vehicle movement, thereby enhancing both safety and user experience

This thesis deals with the analysis and understanding of drivers’ behaviours under car-following. The aim is to enhance the modelling tools toward the development of new ADAS (Advanced Driving Assistance System) logics, characterized by a more human-like behaviour. After having introduced the argument of the thesis (and motivated the work) and having recalled the state of the art most relevant in the field of car-following (as well as in the instruments for observing car-following in the real world), the thesis evolves toward three main sections: actual observation of real-world data and collection of the datasets to be employed for theoretical analysis; theoretical enhancements and propositions; applications to ACC (Adaptive Cruise Control), as a relevant field for ADAS. The data employed in this work have been collected in three different field surveys, two of them carried out in Italy and the other in the United Kingdom. In all cases data have been collected by instrumented vehicles, equipped in such a way to observe and record car-following trajectories. Data have been framed into different theoretical paradigms in order to both validate each theory and to establish the links between these theories. Links have been established both in a formal way (through theoretical investigation) and in a data-driven way. The considered theoretical paradigm for modelling car-following follows different approaches: one is based on the psycho-physical approach and two others are based on an engineering-inspired approach. In particular, the considered psycho-physical approach has been the Action Point theory (Wiedemann, 1974); a revised version of the paradigm, more compliant with the original version of Barbosa (1961) and Todosoiev (1963) has been proposed and justified with reference to the collected data. The first engineering paradigm has been based on a state-space approach. The proposed approach has been shown to be consistent with the Action Point theory. The parameters of the model have been estimated by means of the collected data and the obtained results have been discussed; they are consistent with observations and justify the adopted model. The other engineering model is based on a linear approximation (at any time t, in a discrete-time approach) of the response of the follower to the leader’s stimuli. Also the linear model is shown to be a very good approximation of the observed data; moreover, it has been shown to lead to an harmonic oscillation around the desired spacing at steady-state. This oscillation is consistent with both the Action Point theory and (partially) with the proposed state-space approach. The linear model is particularly suitable for real-time ACC-oriented application; thus it is the model employed in section 4 of this work, where a fully-adaptive ACC system is developed, able to actuate a driving-style actually consistent with driver’s expectations and preferences.