The organization of behavior: A neuropsychological theory

The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the performance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and improper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.

Optimal choice of parameters for particle swarm optimization

The concept of Internet of Things (IoT) can be utilised in vehicles, since the number of sensor nodes in vehicles is rising tremendously because of the uplifting demand of applications for security, safety and convenience. In order to establish the communication among these nodes inside a vehicle, a controller area network with wired architecture provides a prominent solution. However, this solution is not flexible because of the architectural complexity and the demand for a large number of sensors inside the vehicle; hence wired architectures are replaced by wireless ones. Moreover, scalability will be an 
important issue while introducing the IoT concept in Intra-Vehicular Wireless Sensor Networks (IVWSNs). In this paper, a comprehensive performance investigation on the IoT enabled IVWSNs (IoT-IVWSNs) to be carried out in order to address this issue. The overview of the IoT-IVWSNs with a comparative study of the existing technologies and the design challenges for such network are provided. The link design between an end-device and the control unit is analysed, and the performance of the network has been investigated and some open research issues are addressed. It reveals that the delay in packet transmission increases due to higher traffic loads and the number of end-devices. This result demonstrates that the existing MAC protocol works well for a small network (i.e., a network with a maximum number of 50 nodes) but is not suitable for a large network (i.e., a network with more than 50 nodes). The outcome of this research helps to design a smart car system.

/pdf/a-performance-investigation-on-iot-enabled-intra-vehicular-1irzfmyv32.pdf

A Performance Investigation on IoT Enabled Intra-Vehicular Wireless Sensor Networks

In recent decades, there has been increased interest among both transportation researchers and practitioners in exploring the application of artificial intelligence (AI) paradigms to address the real-life problems in order to improve the efficiency, safety and environmental compatibility of transportation systems. In this paper, our main interest is to solve transportation problem by considering the multimodal transport systems and then utilize it to solve neural network (NN) problem in AI. The multimodal transportation problem (MMTP) is nothing but a linear programming problem, and so it is easy to solve by any simplex algorithm. To analyze the proposed method, a numerical example is included and solving it we reveal a better impact for analyzing the real-life decision-making problems. Thereafter, we revoke our approach for solving NN problems, which enhances a connection between MMTP and NN problems. Finally, conclusion and future research directions are presented regarding our study.

Analyzing multimodal transportation problem and its application to artificial intelligence

The motion status recognition of the preceding vehicle in a long-distance region is a requirement for autonomous vehicles to make appropriate decisions and increase their comprehension of the environment. At present, the lane change behavior of the leading vehicle at a short distance is detected using stereo cameras and LiDAR. However, the short detection distance (about 100 m) does not meet the requirements of high-speed driving of autonomous vehicles on expressways; this is a fundamental problem limiting the development of autonomous vehicles exhibiting human-like behavior. In this paper, a comprehensive model consisting of a back-propagation (BP) neural network model optimized by a particle swarm optimization (PSO) algorithm, and a continuous identification model is developed based on the results of naturalistic on-road experiments using millimeter-wave radar data. By considering different time-to-lane crossings (TLCs), the PSO-BP neural network model is trained using real vehicle lane change data and implemented when the TLC of the leading vehicle is longer than 1.0 s. In contrast, when the TLC is less than 1 s, the continuous recognition model of the TLC is used. By comparison with the BP neural network model, the recognition accuracy rate of the proposed model is increased from 80% to 87% after the PSO optimization for a time window of 1.0 s; these results meet the recognition requirements of the autonomous driving systems for distant targets. The findings of this paper improve the cognitive competence and safety of autonomous driving systems.

/pdf/cognitive-competence-improvement-for-autonomous-vehicles-a-4vl49p8ubs.pdf

Cognitive Competence Improvement for Autonomous Vehicles: A Lane Change Identification Model for Distant Preceding Vehicles

In this paper, a new optimal adaptive controller for the active front steering control of a vehicle is proposed. Due to the availability and applicability of proportional-integrator-derivative (PID) controllers, this controller is picked up; but, to overcome its limitations, two optimization and adaptation schemes are employed. The reference transfer function between the yaw rate of a typical vehicle and its steering angle is derived. The actual dynamics is simulated using CarSim toolbox of MATLAB. Best vehicle handling was aimed to be reached for three famous driving manoeuvres by proposing an efficient but economic controller. An optimization is done on the PID coefficients for a specific road condition using the honey bees' algorithm, and then a two-layer artificial neural network is trained using the back propagation learning rule to adjust the controller coefficients for any arbitrary road conditions. The uncontrolled, desired, optimized controlled, and optimized plus trained controlled yaw rate of vehicles are drawn for three manoeuvers and three road conditions. The integral of squared error between the desired and actual trajectories for different manoeuvers and road conditions are evaluated and compared between each other. The performance of the proposed PID controller that was optimized by Bees Algorithm and trained by a neural network was proved. It is noted that the optimized PID controller is good for all road conditions but not excellent. The more we move away from the reference road (in which, the optimization is done), the error will be larger. Thus for other conditions, using the artificial neural network can help decrease the error significantly.

/pdf/integrated-bees-algorithm-and-artificial-neural-network-to-4r2xelzk5y.pdf

Integrated bees algorithm and artificial neural network to propose an efficient controller for active front steering control of vehicles

Due to fast variation of desired variables in vehicle handling problem, design of an accurate applicable economic and considerably quick responsible controller for steering control of such systems has attracted much attention in the literature. The problem becomes more complicated, if the variation of road condition comes into play also. In this study, by combination of a simple PID and an optimized adaptive neural network controller, an arrangement for active front steering control of vehicles in different road frictions is proposed. A general PID controller is picked up and then optimized using the particle swarm optimization algorithm. After that, a neural network is added consecutively and trained by the outputs of PID controller and neural network toolbox of MATLAB software. The proposed controller fulfills both the applicability and efficiency due to dual use of PID and neural network controllers. Simulation results confirm the rightness of suggested controller in active steering control of vehicles even for unpredictable road friction.

Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions

B. Aalizadeh

Papers

Integrated bees algorithm and artificial neural network to propose an efficient controller for active front steering control of vehicles

Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions