Fuzzy modular autonomous intelligent cruise control (AICC) system

The levels of driver comfort and acceptance for an autonomous intelligent cruise control (AICC) system driven in an actual highway environment are investigated. Objective measures of driving performance and behaviour are compared with participants' subjective assessments when operating under manual control, conventional cruise, and AICC. Included in the comparison are measures of driver velocity and braking behaviour. Participants drive slightly higher mean velocities under the manual condition as compared with AICC. Participants applied the brakes less frequently when driving manually. Participants rated the AICC system favorably for comfort, ease of use, and convenience. However, participants did express limited concerns associated with the use of AICC. (A) For the covering abstract of the conference see IRRD 875189.

Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.

A collision model for safety evaluation of autonomous intelligent cruise control

<div class="section abstract"><div class="htmlview paragraph">Developing a knowledge-based predictive driver-aid system requires a strategy that meets the needs and goals, as well as rigorous methods. This paper presents the strategy adopted and the first results obtained in the development of the Navigation Aided Intelligent Cruise Control (N.A.I.C.C.) system. Once the vehicle is located on the route, this new copilot can: <ol class="list nostyle"> <li class="list-item"> <span class="li-label">1.</span> <div class="htmlview paragraph">define the travel direction and the vehicle speed,</div></li> <li class="list-item"> <span class="li-label">2.</span> <div class="htmlview paragraph">determine the distance to the next bend and its characteristics,</div></li> <li class="list-item"> <span class="li-label">3.</span> <div class="htmlview paragraph">predict the optimal speed to negotiate the next bend considering the road profile, the constraints given by the driver and the information provided by the sensors mounted on the vehicle.</div></li></ol></div><div class="htmlview paragraph">Then, used in the warning mode (“Driver Alarm Mode”), N.A.I.C.C. informs the driver of the danger of the situation (excessive speed) or, in the intervention mode (“Velocity Control Mode”), N.A.I.C.C. adapts the speed to the predicted reference. In this latter operating mode, N.A.I.C.C. can be considered as an advanced intelligent cruise control system. Real experiments were carried out with the laboratory test car to validate each module described.</div></div>

An intelligent cruise control system based on access to the conventional cruise control system as well as to the automatic transmission ECU has been realized. The system is characterized by different states (e.g. acceleration, hold speed, deceleration). For state control classified and weighted values of distance, relative speed and vehicle speed information are used. Control strategy therefore is very similar to fuzzy control. Practical results showed high system stability.

This paper describes the safety evaluation results from a Field Operational Test (FOT) of an Intelligent Cruise Control (ICC) system. The primary goal of this evaluation was to determine safety effects of the ICC system. Safety surrogate measures were established and examined for normal driving situations as well as for safety-critical situations. It was found that use of the ICC system in the FOT was generally associated with safer driving compared to manual control and is projected to result in net safety benefits if widely deployed. (A) For the covering abstract see ITRD E106371.

Integrating self-health awareness in autonomous systems

Intelligent cruise control systems and traffic flow stability

Ensemble of long short-term memory prediction and variable universe fuzzy control for seismic response reduction using electromagnetic dynamic vibration absorber

An ex ante impact assessment of advanced driver assistance systems was conducted with focus on autonomous intelligent cruise control and intelligent speed adaptation. The effects of these systems on efficiency, reliability, driving comfort, and safety were addressed by microsimulation for different penetration levels and bottleneck layouts. The deployment of cruise control improves bottleneck capacity. The bottleneck reliability, however, deteriorates in most cases. No significant changes in traffic safety, expressed by the time drivers are exposed to small time-to-collision values, could be established. From the simulation experiments it appears that intelligent speed adaptation has no effect on capacity and provides no substantial contribution to bottleneck reliability. Contrary to expectations, no significant safety benefits could be established using the study’s assessment approach.

This paper investigates the possible virtue of the modification of longitudinal and lane-change behaviors of drivers by intelligent cruise control systems that augment individual driver behavior by enforcing minimum separation between vehicles. Such systems would not only reduce collisions but may also improve traffic flow by reducing the frequency of bottlenecks on freeways. This hypothesis is tested using a modified microsimulation of a length of freeway in Los Angeles County. A transit-oriented minimum time headway controller is compared to a traditional minimum separation intelligent cruise controller. The results show that using a fixed distance policy to control the separation tends to keep the flow more stable during peak periods and reduces travel times.

Обоснован перечень задач самообучения интеллектуальных систем управления автономными роботами и мультиагентными робототехническими группировками. Обсуждаются основные проблемы создания интеллектуальных самообучающихся систем управления. Показано, что один из наиболее перспективных подходов к реализации процессов самообучения в интеллектуальных системах управления автономными роботами и многоагентными робототехническими группировками связан с использованием метода автоматического построения деревьев классификации. Приводятся результаты комплексного моделирования.

With the progress of computer technology and intelligent control system, it is necessary to introduce the PLC together with the current systems. In this paper, we conduct research on the new intelligent industrial control and communication system based on PLC technique. As Industry 4.0 at the core of intelligent factory, the products made in its basic idea is integrated with dynamic digital memory, perception and communication ability, carrying the whole supply chain and all kinds of information required for life cycle. PLC biggest characteristic is to constitute the traditional relay model computer software technology, formed a unique style in relay ladder graph based image programming language, the PLC control system in the form of program to achieve its control function. In the simulation section, we implement our proposed system in detail and conduct numerical analtsis and simulation. The result is acceptable and accurate.

An adaptive controller is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the cerebellar model articulation controller (CMAC). Due to the better ability of handling uncertainties provided by type-2 fuzzy sets, it can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, an intelligent control system is proposed; this control system is comprised of a T2FCMAC and an auxiliary compensation controller. The T2FCMAC is utilized to approximate a perfect controller and the parameters of T2FCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and the T2FCMAC. Finally, a Chua's chaotic circuit is performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance.

Simulation of vehicle traffic on an automated highway system