CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

For the past few years, the automation of transportation becomes a hot research topic for smart cities. Intelligent Transportation System (ITS) aims to manage and optimize the traffic congestion, road accidents, parking allocation using Autonomous Vehicles (AV) system, where the AVs are internally connected for message passing and critical decision making in time-sensitive applications. The data security in such applications can be offered using Blockchain (BC) technology. But, as per the existing literature, there exists no system which can call AVs automatically based on the situation, i.e., call an ambulance in case of an accident, call logistic service in case of home transfer, and call the traffic department in case of traffic jam. Motivated from the aforementioned reasons, in this article, we propose a BC-based secure and intelligent sensing and tracking architecture for AV system using beyond 5G communication network. Recently, AVs are facing issues with sensing and tracking technology as well as the data thefts. AV system contains sensitive information and transfers it through a communication channel to Connected AVs (CAVs), where the corrupted information or delay of a fraction of a second can lead to a critical situation. So, here we present possib the attacks and safety countermeasures using BC technology to protect the AV system. The proposed architecture ensures secure sensing and tracking of an object through BC by deploying AI algorithms at the edge servers. Also, the beyond 5G network enables communications with low latency and high reliability to meet the desires of the aforementioned time-sensitive applications. The proposed system is evaluated by considering the parameters as mobility and data transfer time against the traditional LTE-A and 5G communication networks. The proposed system outperforms traditional systems and can be suitable for diverse applications where latency, reliability, and security are the prime concerns.

Nowadays, the development of modern technologies in the transportation area has increased rapidly. The latest technology is focused on autonomous vehicles. The most important aspect in autonomous vehicle control systems is safety, smooth operation and comfort. It can be provided by the natural element which is from human behavior as a reference to the autonomous control system. This paper is briefly describing the human behavior during braking for autonomous electric vehicle (AEV) control system development. To obtain the human behaviour during accelerating and braking, two units of angle sensors were installed to the accelerator and brake pedal of an electric car to measure the angle of both pedals. Real-time speed is recorded by using a GPS unit. The experiment is focused on braking characteristic of the electric vehicle. It was carried out based on four different distances with fixed initial speed before braking action was recorded. A software interface was designed to display and control real-time speed and pedal angle value during operation.

Integrating self-health awareness in autonomous systems

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

Autonomous Control and Stabilization System for Unmanned Aerial Vehicles

The automotive industry is developing trends in autonomous driving and connected vehicular systems. These vehicles can access and send the data, download the software updates, connect with other vehicles or other IoT devices via the internet or wireless communication. Autonomous vehicle control urges very strict requirements about the security of the communication channels used by the vehicle to exchange information and the control logic that performs complex driving tasks. So, the increased connectivity results in a heightened risk of a cyber-security attack. For maintaining the advances in safe communication, it is important to establish strong security for connected vehicular systems. For this, existing cybersecurity attacks must be considered to minimize future cybersecurity risks in the connected and autonomous vehicle systems. In this chapter, the authors will emphasize recent works on how autonomous vehicles can ensure strong operation under ongoing cyber security attacks and their possible solutions.

According to the world health organization, there were 1.25 million road accident deaths globally in 2013. To decrease this count, it has been said that autonomous vehicles can play a major role. The autonomous vehicle uses different sensors such as LIDAR, RADAR, Ultrasonic sensors, and cameras for detection of lane lines, objects, traffic sign, traffic lights, and driving path. There are several successful projects on autonomous vehicles, but the main limitations are high costs and regulations. The camera can be a cost-effective solution as compare to LIDAR in all the basic detection needed for autonomous driving. The proper placement of sensors on the vehicle plays an important role in efficient design, working and reducing the cost of the overall autonomous vehicle system. This paper discusses the parameters considered while deciding the positioning of the camera, and the geometric calculation for the best positioning of a monocular camera on an autonomous vehicle.

For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems.

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals. This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system. The terrain analysis system consists of five integrated processing stages. Each process is discussed in detail and supported by a number of mission oriented examples. Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3]. For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force. Civilian applications for such a device entail the investigation of hazardous areas (e.g. nuclear reactors and toxic waste dumps), search over large areas (e.g. downed aircraft or lost campers) and monitoring of large facilities (e.g. robot security police). As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature. The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action. This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter program.System OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage. Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate.

Underwater imaging is being increasingly helpful for the autonomous robots to reconstruct and map the marine environments which is fundamental for searching for pipelines or wreckages in depth waters. In this context, the accuracy of the information obtained from the environment is of extremely importance. This work presents a study about the accuracy of a reconfigurable stereo vision system while determining a dense disparity estimation for underwater imaging. The idea is to explore the advantage of this kind of system for underwater autonomous vehicles (AUV) since varying parameters like the baseline and the pose of the cameras make possible to extract accurate 3D information at different distances between the AUV and the scene. Therefore, the impact of these parameters is analyzed using a metric error of the point cloud acquired by a stereoscopic system. Furthermore, results obtained directly from an underwater environment proved that a reconfigurable stereo system can have some advantages for autonomous vehicles since, in some trials, the error was reduced by 0.05m for distances between 1.125 and 2.675 m.

Our research primarily focuses on developing mechanisms to intelligently route autonomous vehicles in an urban area while managing to reduce traffic congestion. We propose an Intelligent Transportation System for Autonomous vehicles based on Multi-Agent paradigm. These agents observe traffic conditions over a period of time. Historical traffic information along with information of factors that affects traffic(like weather conditions) are used to develop a regression based learning algorithm that predicts time required to traverse a route for a given time-slot, which can be used to find out shortest path for a given source-destination pair, before beginning to travel. This estimated traverse time for every lane is communicated to the vehicles. Based on this information, the vehicles find out k shortest paths. Reservation based protocols are used to obtain a single path such that the overall congestion is minimized and the destination is reached in the shortest possible time. The shortest path obtained is then traversed with the help the vehicle control system with optimized fuel efficiency, ensuring vehicle stability and obstacle avoidance. To achieve this path trajectory, we used nonlinear Model Predictive control (MPC) approach based on bicycle model of vehicle dynamics. The main advantage of MPC is the fact that it allows the current time-slot to be optimized, while taking future time-slots into account. We further investigate the linear MPC strategies for Co-operative adaptive Cruise control which directly minimizes the fuel consumption.

Autonomous vehicle control based on receding horizon method

Autonomous driving vehicle control network by edge computer having very short response time has been constructed. Each vehicle has an agent program on the edge and communicated with adjacent vehicle agent in the cyber network. The advantage of connected vehicle control from stand-alone vehicle is sophisticated and total/ global optimizing control. The network edge computer calculated “social welfare” which is the best benefit for all vehicle in the society. This paper describes control architecture and method at edge computer. In addition, some experimental results for controlling autonomous vehicle are also described.

Traffic congestion has become increasingly prevalent in many urban areas, and researchers are continuously looking into new ways to resolve this pertinent issue. Autonomous vehicles are one of the technologies expected to revolutionize transportation systems. To this very day, there are limited studies focused on the impact of autonomous vehicles in heterogeneous traffic flow in terms of different driving modes (manual and self-driving). Autonomous vehicles in the near future will be running parallel with manual vehicles, and drivers will have different characteristics and attributes. Previous studies that have focused on the impact of autonomous vehicles in these conditions are scarce. This paper proposes a new cellular automata model to address this issue, where different autonomous vehicles (cars and buses) and manual vehicles (cars and buses) are compared in terms of fundamental traffic parameters. Manual cars are further divided into subcategories on the basis of age groups and gender. Each category has its own distinct attributes, which make it different from the others. This is done in order to obtain a simulation as close as possible to a real-world scenario. Furthermore, different lane-changing behavior patterns have been modeled for autonomous and manual vehicles. Subsequently, different scenarios with different compositions are simulated to investigate the impact of autonomous vehicles on traffic flow in heterogeneous conditions. The results suggest that autonomous vehicles can raise the flow rate of any network considerably despite the running heterogeneous traffic flow.

This paper is concerned with the security sliding mode-based leader-following control of multiple autonomous unmanned underwater vehicles (AUVs) system subject to stochastic DoS attacks. Firstly, a Bernoulli variable is used to describe the phenomenon of stochastic DoS attacks between controllers and actuators, and the attack probability is known for controller design. Secondly, the closed-loop system is established by taking the integral sliding mode control scheme and stochastic DoS attacks into account. Then, the conditions of stochastic stability for system are given by using the stochastic systems analysis method and the model decomposition technique. In the simulation part, a group of AUVs models is used to verify the validity of the proposed state consensus control scheme. The simulation example shows that all the state can achieve consensus even in presence of randomly launched DoS attack.