Time triggered customized scheduler for adaptive cruise control application in vehicle

In every day our daily needs we are depending on transport system. Transport mode may be two wheeler, four wheelers etc. In metro there is a huge traffic of vehicles, there may be chances of accidents. The most of the accidents are due to inability of the driver to control the vehicle. In order to improve the safety of the driver, need to add some auto control for the vehicle. The driver assistance is one of the auto control system. In driver assistance there are many subsystems. Adaptive cruise control is one of the major driver assistance sub systems. The Adaptive cruise control was implemented by using Real time operating system with preemptive priority based scheduler. It takes more memory and time. Time triggered cooperative scheduler is one of the most efficient and predictable scheduler. It is designed for controlling adaptive cruise control tasks. It is implemented using ARM7 based Microcontroller, ultrasonic sensor, Throttle valve sensor and speed sensor. The whole system is placed in host vehicle, vary the speed of the host vehicle so that to keep minimum distance between host and target vehicle. The TTC scheduler for adaptive cruise control is simulated using keil IDE tool. The application tasks are controlled by using efficient Time triggered cooperative scheduler. The adaptive cruise control is controlled by TTC scheduler is written in embedded `c'. The simulated results are compared with existing cooperative schedulers. It is proved from the results that the TTC scheduler takes less time and less memory.

<div class="section abstract"><div class="htmlview paragraph">In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics.</div><div class="htmlview paragraph">The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems.</div><div class="htmlview paragraph">Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle. Some high precision sensors were mounted in both test vehicles, such as RT 3002<sup>®</sup>, RT range<sup>®</sup>, radar and pedal pressure sensor. The traffic vehicle was set in different driving modes, include cruise mode, decelerating slowly, accelerating slowly, hard braking, hard acceleration, cutting out the lane, cutting into the line and sinusoidal movement. The host vehicle was operated behind the traffic vehicle by 84 drivers selected randomly. The drivers’ driving styles were evaluated via both subjective and objective indicators. The subjective indicators were based on the questionnaires by interviewing the drivers and the passengers. The objective indicators were based on the parameters extracted from the specific test conditions. The vehicle-following data were collected and analyzed via MATLAB<sup>®</sup> which was further trained by Gaussian Mixture Model. With regarding to the multi-mode switching strategy, the parameters of different driver’s characteristics were considered in the fuzzy PID control method to improve the adaptivity and accuracy in the switching process. Meanwhile, different fuzzy rules and fuzzy membership functions were applied for every type of drivers to calculate the best parameters for the personalized ACC control strategy. The control modes include cruise, steady following, approaching and hard braking. The different driving characteristics were also added into every control strategy via road experimental data and analyzed results from Gaussian Mixture Model. Compared with the existing ACC, the system proposed in this paper has great advantages on not only the adaptivity and human acceptance, but also the performance of the system itself which was verified on the simulation platform PanoSim<sup>®</sup>.</div></div>

Workload and distraction are problems that affect road driving and can cause very serious accidents. Drive assistance systems are very important to help drivers relieve fatigue of driving by increasing road safety and reducing the number of accidents. The Adaptive Cruise Control (ACC) is a useful driver assistance system to set and maintain a safe distance from the front vehicle, modulating speed and limiting sudden braking. However, the effects of the ACC on overall driver behaviour have not yet been thoroughly investigated. In this study, the authors assessed the influence of ACC on mental workload and attention of drivers in a real driving environment. The results of several evaluations were considered: subjective assessment of workload (NASA-TLX), physiological measurements of workload (brain activity through electroencephalographic technique and the analysis of visual behaviour through an eye monitoring device) and performance-based measurements (via Vbox Pro Video mounted on the vehicle). 52 drivers were involved in the study, 26 ACC experts and 26 non-experts, who drove along the ring road of Bologna (Tangenziale di Bologna) (Italy), both using ACC and manually, without the help of any device. During the test a secondary task was introduced: the sudden arrival of a braking car. Results showed that the use of ACC increased distraction when driving.

• 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

Connected vehicles transmit and receive basic safety messages that enable the use of safety applications intended to avoid crashes. The information age (IA) is the latency between the basic safety message being generated by the transmitting vehicle and received by another vehicle. The receiving vehicle’s processing time is included in the IA but, at several milliseconds, can be ignored. The IA of vehicle-to-vehicle communications needs to be kept beneath a certain threshold so as not to compromise the effectiveness of safety applications. Forward collision warning (FCW) requires a critical IA in scenarios in which a lead vehicle brakes hard and abruptly. An analytical model of FCW, with the lead vehicle coming to a complete stop, is proposed. For the host vehicle behind the lead vehicle to stop safely, an appropriate IA, perception–reaction time, and deceleration rate are required. Sensitivity analysis of the IA is performed to provide the probabilities of a safe stop for various combinations of speed and deceleration rate. Monte Carlo simulations are implemented to generate the probabilities, and perception–reaction times, speeds, and deceleration rates are formulated as probability density functions. The results show that the IA is a sensitive factor in the safe operation of an FCW application. The IA for a low-speed host vehicle is more sensitive than the IA for a high-speed host, when the host and lead vehicles are both traveling at similar speeds with a normal clearance gap. The IA is more sensitive when the host vehicle speed is low (≤40 mph) or when the lead vehicle’s deceleration rate is high (≥0.5 g).

In this paper, a pure electric vehicle (PEV) equipped with adaptive cruise control (ACC) system is studied for a vehicle‐following process. And a multiobjective optimization algorithm for ACC system is proposed in a model‐predictive control (MPC) framework for optimizing safety, tracking capability, driving comfortability and energy consumption. The longitudinal dynamics of the ACC system are modeled, which not only considers the vehicle spacing and speed, but also introduces the acceleration and the change rate of acceleration (jerk) for the host vehicle and fully considers the influence of the acceleration of the leading vehicle. The improvement of driving comfortability and the reduction of energy consumption are achieved mainly by optimizing the acceleration and jerk of host vehicle. Some optimized reference trajectories are introduced to MPC for improving driving comfortability of host vehicle. The performances of the multiobjective upper level algorithm combined with the PEV model are evaluated for three representative scenarios. The results demonstrate the effectiveness of the proposed algorithm.

To address the problem caused by a conventional adaptive cruise control (ACC) system, which hinders drivers from changing lanes, in this study we propose a novel coordinated ACC system with a lane-change assistance function, which enables dual-target tracking, safe lane change, and longitudinal ride comfort. We first analyze lane-change risk by calculating minimum safety spacing between the host vehicle and surrounding vehicles and then develop a coordinated control algorithm using model predictive control theory. Tracking performance is designed on the basis of tracking errors of the host car and two leading vehicles, safety performance is realized by considering the safe distance between the host car and surrounding vehicles, and ride comfort performance is realized by limiting the vehicle's longitudinal acceleration. Driver-in-the-loop tests performed on a driving simulator confirm that the proposed ACC system can overcome the disadvantages of conventional ACC and achieves multiobjective coordination in the lane-change process.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

This paper presents a method of vehicle lateral collision avoidance based on receding horizon strategy. In this paper, the vehicles’ position and velocity information is obtained based on the millimeter-wave radar, the global satellite positioning system (GPS) and the vehicle to vehicle communication technology. Then the method of transforming the vehicles’ position and speed to the host vehicle coordinate system is given. The trajectory prediction model and collision recognition algorithm of the host vehicle and the other vehicle are established based on the kinematic model. The receding horizon control algorithm is used to control the host vehicle speed in order to avoid the coming collision. The simulation results show that the host vehicle can achieve the goal of lateral collision avoidance by receding horizon control algorithm under the condition of constant and changed vehicle speed.

The Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) are the prime land and autonomous vehicles navigation information source. However, urban canyons high rise buildings block the GPS signal. Therefore, the Inertial Navigation System (INS) or the Reduced Inertial Sensor System (RISS) are utilized as an alternative source of the navigation information. The RISS is able to produce a full navigation solution using less number of sensors and calculations. Dramatically, the RISS solution drifts over time as the INS system. The integration between the GPS and the RISS is mitigating each system drawbacks. However, during the GPS outage periods, the navigation system is only depending on the inertial measurements. The RISS is utilizing the odometer/speedometer to measure the vehicle's forward speed. Many types of error affect the odometer measurements. These errors are either due to the vehicle's specifications as the differences in wheel diameters, and/or inefficient wheelbase or the road nature as wheel slips, uneven road surfaces, and/or skidding. In this paper, a Radar-based RISS system is introduced to take place the traditional RISS or INS systems. The radar unit is an essential part of the adaptive cruise control (ACC) system. Furthermore, it is capable of measuring the relative velocity and distance between the carrying and the in front vehicle to reduce collisions and increase the safety of driving. In addition, the radar measurements are not affected by the error sources that affect the odometer. The idea is to invest the ground reflection and derive the forward speed of the onboard vehicle as the ACC keep a safe distance between the cars. A novel method based on extracting the ground reflection features is introduced to obtain the onboard vehicle's speed. The obtained speed is utilized with two accelerometers and one vertical gyroscope to produce the Radar-based RISS system. The proposed system has been integrated with the GPS producing a Radar-based RISS/GPS integrated navigation system. The system has been tested on a real road trajectory in a downtown area and involved several GPS signal outages. The results show the significant capabilities of the proposed system in keeping the navigation solution drift to a minimum especially when the GPS signal is in outage compared with the traditional RISS/GPS system.

Adaptive Cruise Control (ACC), Cruise Control (CC), and Automatic Emergency Braking (AEB) serve as the basis of longitudinal automated driving, and as Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab’s Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.

Comprehensive situational awareness is paramount to the effectiveness of advanced driver assistance systems (ADASs) used in daily urban traffic, particularly for the unmarked roads, which cannot fulfill the requirements of conventional ADAS systems. This paper proposed a stereovision-based multimodal ADAS system designed for expanding the usability of ADAS functions, including lane-keeping assist, adaptive cruise control, and precrash system, to normal urban scenarios with unmarked roads. At first, the physical road boundary and vehicle candidates are detected. Subsequently, the contextual information between the host vehicle, the road, and the other vehicles are correlated for both low-level object detection improvement and high-level road structure estimation. Finally, the required ADAS elements are generated based on the correlation results with respect to the system functionalities. Experimental results in various typical but challenging scenarios have substantiated the effectiveness of the proposed system, which could help increase the value of the existing ADAS system without major modifications or expense.

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

Safety and energy are two key issues to affect the development of automotive industry. For the safety issue, the vehicle active collision avoidance system is developing gradually from a high-speed adaptive cruise control (ACC) to the current low-speed stop and go (SG), and the future research topic is the ACC system at full-speed, namely, the advanced ACC (AACC) system. The AACC system is an automatic driver assistance system, in which the driver's behavior and the complex traffic environment ranging are taken into account from high-speed to low-speed. By combining the function of the high-speed ACC and low-speed SG, the AACC system can regulate the relative distance and the relative velocity adaptively between two vehicles according to the driving condition and the external traffic environment. Therefore, not only can the driver stress and the energy consumption caused by the frequent manipulation and the traffic congestion both be reduced effectively at the urban traffic environment, but also the traffic flow and the vehicle safety will be improved on the highway. Taking the actual traffic environment into account, the velocity of vehicle changes regularly in a wide range and even frequently under SG conditions. It is also subject to various external resistances, such as the road grade, mass, as well as the corresponding impact from the rolling resistance. Therefore, the behaviors of some main components within the power transmission show strong nonlinearity, for instance, the engine operating characteristics, automatic transmission switching logic and the torque converter capacity factor. In addition, the relative distance and the relative velocity of the inter-vehicles are also interfered by the frequent acceleration/deceleration of the leading vehicle. As a result, the performance of the longitudinal vehicle full-speed cruise system (LFS) represents strong nonlinearity and coupling dynamics under the impact of the external disturbance and the internal uncertainty. For such a complex dynamic system, many effective research works have been presented. J. K. Hedrick et al. proposed an upper+lower layered control algorithm concentrating on the high-speed ACC system, which was verified through a platoon cruise control system composed of multiple vehicles [1-3]. K. Yi et al. applied some linear control methods, likes linear quadratic (LQ) and proportional–integral–derivative (PID), to design the upper and lower layer controllers independently for the high-speed ACC system [4]. In ref.[5], Omae designed the model matching control (MMC) vehicle high-speed ACC system based on the H-infinity (Hinf) robust control method. To achieve a tracking control between

Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control