Intelligent Vehicle Technologies Research Articles

Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents

Emergency vehicles (EVs) are at high risk of accidents during emergency driving. To make use of countermeasures to mitigate these risks, it is important to understand under what circumstances EV-related accidents occur. The common risk factors for EV-related accidents were examined through a systematic literature review. A total of 22 articles were examined for risk factors associated with EV-related accidents. The most reported risk factors were, in order of frequency, intersections, daytime, dry roads, clear weather, urban roads, traffic signals, and angular collisions. The articles were also reviewed for suggested countermeasures to mitigate the risk factors. The most commonly suggested countermeasures were driver training for EV operators, educating the public, exercising caution at intersections, wearing a seatbelt, and intelligent vehicle technologies. Cooperative intelligent transport systems (C-ITSs) have the potential to mitigate the risks of EV-related accidents. Therefore, three C-ITS services were investigated: EV approaching, EV preemption, and geofencing. They could all be used to inform, warn, or control aspects of driving. Each suggested service has the potential to decrease risk factors for EV-related accidents. The current literature review provides guidance on under what circumstance and in what form C-ITSs could be beneficial to prevent EV-related accidents. Further research is needed to examine behavior when drivers are introduced to C-ITSs.

Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway

The recognition of the preceding vehicle lane-changing manoeuvre (LCM) is essential for improving the rationality and safety of the decision-making of driverless vehicles. However, traditional recognition researches for preceding vehicle LCM are generally characterized by problems such as low recognition accuracy and poor research foundation. In response to these problems, this paper carried out naturalistic driving study (NDS) on the highway, from which a large amount of on-road data consisting of lane-keeping (LK) and lane-changing left and right (LCL, LCR) manoeuvres were collected. A stacking-based ensemble learning method for the recognition of the LCM of the preceding vehicle, which integrates the random forest, support vector machine, long and short-term memory network (LSTM), and bi-directional LSTM based on attention mechanism (AT-Bi-LSTM) algorithms, is proposed. Compared with traditional machine learning methods, the proposed method exhibits greater advantages in terms of its recognition accuracy. It is particularly of note that the recognition accuracy of the model at 0.4 s and 0.8 s after the LCM reached 90.77% and 95.54%, respectively. The study reported is of great significance for the construction of more intelligent vehicle-vehicle collaboration and the promotion of the industrial applications of intelligent vehicle technology.

Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country

The use of unmanned aerial vehicles (UAVs) in various spheres of human activity is a promising direction for countries with very different types of economies. This statement refers to resource-rich economies as well. The peculiarities of such countries are associated with the dependence on resource prices since their economies present low diversification. Therefore, the employment of new technologies is one of the ways of increasing the sustainability of such economy development. In this context, the use of UAVs is a prospect direction, since they are relatively cheap, reliable, and their use does not require a high-tech background. The most common use of UAVs is associated with various types of monitoring tasks. In addition, UAVs can be used for organizing communication, search, cargo delivery, field processing, etc. Using additional elements of artificial intelligence (AI) together with UAVs helps to solve the problems in automatic or semi-automatic mode. Such UAV is named intelligent unmanned aerial vehicle technology (IUAVT), and its employment allows increasing the UAV-based technology efficiency. However, in order to adapt IUAVT in the sectors of economy, it is necessary to overcome a range of limitations. The research is devoted to the analysis of opportunities and obstacles to the adaptation of IUAVT in the economy. The possible economic effect is estimated for Kazakhstan as one of the resource-rich countries. The review consists of three main parts. The first part describes the IUAVT application areas and the tasks it can solve. The following areas of application are considered: precision agriculture, the hazardous geophysical processes monitoring, environmental pollution monitoring, exploration of minerals, wild animals monitoring, technical and engineering structures monitoring, and traffic monitoring. The economic potential is estimated by the areas of application of IUAVT in Kazakhstan. The second part contains the review of the technical, legal, and software-algorithmic limitations of IUAVT and modern approaches aimed at overcoming these limitations. The third part—discussion—comprises the consideration of the impact of these limitations and unsolved tasks of the IUAVT employment in the areas of activity under consideration, and assessment of the overall economic effect.

Preface

International Virtual Conference on Intelligent Robotics, Mechatronics and Automation Systems (IRMAS 2021) March 26-27, 2021Mechatronics and Robotics deal with the synergistic incorporation of diverse disciplines, such as mechanical engineering, electrical engineering, manufacturing engineering, computer and software to solve complex problems. Intelligent Mechatronics Systems transform world towards complete automation. The transformations include industrial automation (Industry 4.0), smart manufacturing systems, connected machines, Intelligent and autonomous vehicle technologies, autonomous and service robots, etc. These technological developments drive us into the future where almost everything will be automatic.The conference is jointly organized by the Centre for Automation and School of Mechanical Engineering, Vellore Institute of Technology (VIT), Chennai, India in Virtual Mode. This Virtual Conference received overwhelming response from Participants from all parts of India and many other countries Malaysia, Bangladesh, Indonesia, Turkey and Morocco. Out of the 140 papers received, 110 papers been accepted after scrutinizing and peer Review process. This includes 20 Papers from outside India.Due to the current pandemic situation, which made situation not suitable for transport and gathering, the conference has been organized completely in Virtual Mode. The entire conference is conducted using video conferencing platform-Zoom. It enabled all the speakers to present live for the audience and answer all the questions posted to them in an efficient way. This virtual conference enabled the researchers and students to present their work from their home/workplace in the testing time due to the pandemic. A total of 127 participants has participated in this two-day conference.The conference hosted four keynote lectures each spanning 60 minutes each including live Q&A sessions. A total of 110 papers has been presented in 14 sessions across two days of the conference. Each paper has been allocated 12 Minutes for presentation and 3 minutes for Q&A. Among the presented 110 papers, 71 papers have been scrutinized for publication.List of Keynote Speakers, Convenors, Organizing Secretary, Organizing Committee, Technical Committee are available in this pdf.

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today’s era. This research article reviews traffic actors’ behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles’ intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors’ knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation.

Thank You to All Volunteers [President’s Message

The IEEE Intelligent Transportation Systems (ITS) Society’s flagship conferences take place in summer and fall each year. In July, the 32nd IEEE Intelligent Vehicle Symposium (IV) was successfully organized in Nagoya, Japan, through virtual means with great support from Nagoya University and a number of Japanese partners and organizations. The conference committee, led by Kazuya Takeda, Nagoya University, planned a large-scale demonstration of connected and automated vehicle technologies. Many of us were excited for the opportunity to experience cutting-edge intelligent vehicle technologies showcased at the Tokyo Olympics. Unfortunately, with the COVID-19 pandemic persisting, the demonstration could not happen. The Organizing Committee worked hard to transition the conference into a virtual event that offered engaging interactive experiences. IV’21 received 446 papers; 220 papers plus additional invited talks were given. The presentations are available on YouTube for easy access.

Design of Embedded Intelligent Car Control System Based on Android

With the rapid development of artificial intelligence technology, Unmanned intelligent vehicle technology, more and more people pay attention to it, it will change the way people live and travel, it can be applied to all aspects of urban construction and social development. Aiming at the instability of the intelligent car in the process of automatic driving, an embedded intelligent car control system based on Android is designed. Cortex-A9 is the core of the main control platform of the system, connect seven function modules, and through the data receiving module of the controller, it is connected with the Android application control system of the host computer, each function module can work independently and cooperate with each other, realize the automatic control of intelligent car, the intelligent car can move forward by itself, identify the path, grab the goods, patrol the line, avoid obstacles, and can run smoothly and quickly, at the same time, it can accept the manual control of Android interface.

Risk perception and the warning strategy based on safety potential field theory

Benefiting from the rapid development of communication and intelligent vehicle technology in recent years, most traffic information is capable of being collected, processed, and transmitted to each vehicle through a connected and automated vehicles (CAVs) system. To meet the higher requirements of driving safety in CAVs environment, it is necessary to develop more effective safety evaluation indicators that combine all the traffic information received by the vehicle. To this end, this study proposes a novel methodology for risk perception and warning strategy based on safety potential field model to minimize driving risk in the CAVs environment. A dynamic safety potential field model was constructed to describe the spatial distribution of driving risk encountered by vehicles. This safety potential field model can comprehensively consider the impact of various types of traffic information on driving risk. And then, a novel driving risk indicator, named potential field indicator (PFI), was established to evaluate the level of driving risk. Finally, an early warning strategy was proposed to prevent accidents, whose performance was evaluated by several simulations carried out through SUMO simulator. The comparison with some classic risk indicators indicate that our proposed PFI can more accurately reflect the actual driving risk faced by vehicles under different vehicle motion states and thus is more suitable for driving risk assessment in the CAVs environment. It is expected that the findings in this study could be valuable in improving the performance of strategic decision-making in driver assistance systems in the CAVs environment.

Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

With the development of intelligent vehicle technology, the demand for advanced driver assistant systems kept increasing. To improve the performance of the active safety systems, we focused on right-turning vehicle’s collision warning and avoidance. We put forward an algorithm based on Monte Carlo simulation to calculate the collision probability between the right-turning vehicle and another vehicle (or pedestrian) in intersections. We drew collision probability curves which used time-to-collision as the horizontal axis and collision probability as the vertical axis. We established a three-level collision warning system and used software to calculate and simulate the collision probability and warning process. To avoid the collision actively when turning right, a two-stage braking strategy is applied. Taking four right-turning collision conditions as examples, the two-stage braking strategy was applied, analysing and comparing the anteroposterior curve diagram simultaneously to avoid collision actively and reduce collision probability. By comparison, the collision probability 2 s before active collision avoidance was more than 80% and the collision probability may even reach 100% in certain conditions. To improve the active safety performance, the two-stage braking strategy can reduce the collision probability from exceeding 50% to approaching 0% in 2 s and reduce collision probability to less than 5% in 3 s. By changing four initial positions, the collision probability curve calculation algorithm and the two-stage braking strategy are validated and analysed. The results verified the rationality of the collision probability curve calculation algorithm and the two-stage braking strategy.

Decision Making, Planning, and Control Strategies for Intelligent Vehicles

The intelligent vehicle will play a crucial and essential role in the development of the future intelligent transportation system, which is developing toward the connected driving environment, ultimate driving safety, and comforts, as well as green efficiency. While the decision making, planning, and control are extremely vital components of the intelligent vehicle, these modules act as a bridge, connecting the subsystem of the environmental perception and the bottom-level control execution of the vehicle as well. This short book covers various strategies of designing the decision making, trajectory planning, and tracking control, as well as share driving, of the human-automation to adapt to different levels of the automated driving system. More specifically, we introduce an end-to-end decision-making module based on the deep Q-learning, and improved path-planning methods based on artificial potentials and elastic bands which are designed for obstacle avoidance. Then, the optimal method based on the convex optimization and the natural cubic spline is presented. As for the speed planning, planning methods based on the multi-object optimization and high-order polynomials, and a method with convex optimization and natural cubic splines, are proposed for the non-vehicle-following scenario (e.g., free driving, lane change, obstacle avoidance), while the planning method based on vehicle-following kinematics and the model predictive control (MPC) is adopted for the car-following scenario. We introduce two robust tracking methods for the trajectory following. The first one, based on nonlinear vehicle longitudinal or path-preview dynamic systems, utilizes the adaptive sliding mode control (SMC) law which can compensate for uncertainties to follow the speed or path profiles. The second one is based on the five-degrees-of-freedom nonlinear vehicle dynamical system that utilizes the linearized time-varying MPC to track the speed and path profile simultaneously. Toward human-automation cooperative driving systems, we introduce two control strategies to address the control authority and conflict management problems between the human driver and the automated driving systems. Driving safety field and game theory are utilized to propose a game-based strategy, which is used to deal with path conflicts during obstacle avoidance. Driver's driving intention, situation assessment, and performance index are employed for the development of the fuzzy-based strategy. Multiple case studies and demos are included in each chapter to show the effectiveness of the proposed approach. We sincerely hope the contents of this short book provide certain theoretical guidance and technical supports for the development of intelligent vehicle technology.

Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions

Intelligent vehicle technologies like driver assistance systems and in-vehicle information systems, enhance convenience of the driving experience for drivers and passengers. At the same time, these systems may increase driver distraction and workload. Guidelines developed for this purpose include principles, methods, and assessments which are widely agreed upon, with some being singled out for a particular recommendation or requirement. Especially the display of graphical or photographic images are generally assumed to distract the driver from safely operating the vehicle and should be blocked during driving under all circumstances (so called per se lock outs). This study investigates the effect of displaying graphical and photographical images during driving on driveŕs glance behavior during real-world driving. Findings presented in this paper provide empirical evidence for the unobtrusiveness of these stimuli: Participants didn’t exhibit longer glance durations towards in-vehicle information systems, nor a deterioration of driver distraction parameters such as total eyes off road time and long glance proportion when being compared to driving without displaying any photographic images.

Research on Millimeter Wave Radar Simulation Model for Intelligent Vehicle

Radar simulation models can effectively overcome the drawbacks of real vehicle experiment and speed up the development process of intelligent vehicle technologies based on millimeter wave radar via virtual testing. However, there are still many gaps between the radar model using in the virtual driving environment and the real radar. In this paper, a novel simulation model of intelligent vehicle millimeter wave radar is proposed. Based on the analysis of the real radar performance in typical application scenes, the radar model considers the mechanism and characteristics of the vehicle radar synthetically and a systematic radar modeling architecture with innovation is introduced. The highlights of this radar model include the design of the RCS simulation model for radar targets with both high accuracy and real-time performance, the establishment of the quantitative false alarm model, missed detection model and measurement error simulation model. Vast amounts of data collected by real vehicle radar are applied to fetch model parameters and verify the accuracy of the radar model. Simulation results show that the proposed model can reach both high reliability and computational efficiency.

Detection and recognition method of monocular vision traffic safety information for intelligent vehicles

Intelligent vehicle technology has become a research hot issue in recent ten years, the reason is that intelligent vehicles can not only be used as a flexible weapon platform in the military. And in life, it is also a system that provides convenience and security for people. For example, driverless cars and advanced driver assistance systems (ADAS). Information processing is the key to the degree of intelligence, and the detection and recognition of traffic safety information based on monocular vision is the core of information processing, it’s also the bottleneck problem. Because of the complexity and diversity of the environment have brought great challenges to this problem. In this paper, the existing lane detection methods in structured and semi-structured roads do not specifically consider the problem of weak line detection, two models are proposed. Fuzzy LDA enhancement model is used to enhance the contrast of lane area, another brightness contrast saliency model can be used for robust Lane extraction. Then, two models are applied to lane detection, a two-stage lane detection method is proposed and a blind area vehicle detection method is designed. Firstly, the vehicle area is roughly extracted based on road gray statistics, and then the typical vehicle features are screened finely. Finally, the extracted features and SVM classifiers are used to confirm the candidate regions. Experiments show that: The proposed method can detect the vehicle in the blind area very well and is insensitive to the shape distortion and size change of the vehicle.

Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment

With the development of intelligent vehicle technologies, vehicles can obtain more and more information from various sensors. Many researchers have focused on the vertical and horizontal relationships between vehicles in a vehicle cluster environment and control of the vehicle power system. When the vehicle is driving in the cluster environment, the powertrain system should quickly respond to the driver’s dynamic demand, so as to achieve the purpose of quickly passing through the cluster environment. The vehicle powertrain system should be regarded as a separate individual to research its active control strategy in a vehicle cluster environment to improve the control effect. In this study, the driving characteristics of vehicles in a cluster environment have been analyzed, and a vehicle power-demanded prediction algorithm based on a vehicle-following model has been proposed in a cluster environment. Based on the vehicle power demand forecast and driver operation, an active control strategy of the vehicle powertrain system has been designed considering the passive control strategy of the powertrain system. The results show that the vehicle powertrain system can ensure a sufficient backup power with the active control proposed in the paper, and the motor efficiency is improved by 0.61% compared with that of the passive control strategy. Moreover, the overall efficiency of the powertrain system is increased by 0.6% and the effectiveness of the active control is validated using the vehicle cluster environment.

Optimal control problem of multi‐vehicle cooperative autonomous parking trajectory planning in a connected vehicle environment

The development of intelligent network and intelligent vehicle technology makes it possible for multi-vehicle cooperative driving. In this study, the multi-vehicle cooperative autonomous parking condition is addressed by establishing a vehicle kinematics model combined with dynamic constraints and endpoint and collision avoidance constraints. The autonomous parking trajectory planning problem is transformed into an optimal control problem. The shortest parking completion time is set as the optimal cost function, and the optimal control problem is discretised by using the Gauss pseudo-spectral method. Then, the non-linear programming problem is solved. Moreover, an initial guess generation strategy is proposed, and the iteration method is used to solve the problem by adding collision avoidance constraints to the collision situation of the vehicle to improve the convergence and robustness of the problem. The simulation model is built in MATLAB/Simulink to simulate multi-vehicle cooperative autonomous parking under different working conditions. The simulation results show that the proposed method can solve the multi-vehicle cooperative autonomous parking trajectory planning problem uniformly and effectively. Compared with the traditional pseudo-spectral method, the proposed method has a faster convergence speed.

Design analysis of a decentralized equilibrium-routing strategy for intelligent vehicles

Intelligent vehicle technologies are opening new possibilities for decentralized vehicle routing systems, suitable for regulating large traffic networks, and at the same time, capable of providing customized advice to individual vehicles. In this study, we perform a rigorous simulation-based analysis of an in-vehicle routing strategy that aims to achieve a user-equilibrium distribution in traffic. Novel features of the approach include: a mechanism based on forward propagation of individual vehicle decisions to anticipate future traffic dynamics; time-dependent prediction of route travel times with neural network-based link predictors; and a stochastic routing policy for in-vehicle decision-making based on predicted travel times. However, for an effective application of the approach, design choices need to be made regarding the accuracy of the link predictors, and some control settings. These choices may depend on the network size and structure. We investigate the impact of two important design aspects: sequentially using link-level predictors for route travel time estimation, and the control parameter values, on the equilibrium performance at the network-level. The results suggest functional scalability of the approach, in terms of the prediction model accuracy and routing performance. Overall, the work contributes to a qualitative and quantitative understanding of emergent performance from the given routing approach.

Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints

Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to guarantee $\mathcal {L}_{p}$ string stability, i.e., $\mathcal {L}_{p}$ -norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees $\mathcal {L}_{p}$ string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints.

MicroIV: A Cooperative Driving Hardware Simulation Platform for Cooperative-ITS

Cooperative-ITS (C-ITS) is a rising innovation that involves the integration of existing intelligent vehicle technology and communication technology, which allows information exchange among components and autonomous cooperative driving among vehicles. Although some hardware platforms are developed for simulation of C-ITS applications, there are still some problems such as lack of global positioning, limited simulation scenarios, and high cost. This paper presents Micro Intelligent Vehicle (MicroIV)-based cooperative driving hardware simulation platform for simulation of C-ITS applications, especially the cooperative maneuvers. Different from existing platforms, cameras installed around the test field are used to capture the motion and global positioning information of micro vehicles, similar to GPS methods. Such system architecture reduces the vehicle's hardware configuration and significantly cuts the cost of the whole system. Then, it is easy to expand the scale of the experiments. Therefore, the proposed platform is flexible for different cooperation scenarios. Point-to-multipoint communication structure with the fixed master node is used to implement the communication between vehicles. Interacting Multiple Model method is leveraged to improve positioning accuracy. Experiments are conducted to prove that the proposed MicroIV platform can meet the positioning accuracy requirement of C-ITS application. Then, applications for both V2V and V2I cooperation are presented: platoon with 20 vehicles, intersection cooperation with 8 vehicles, merging, and automatic parking. These experiments prove the feasibility and reliability of the proposed platform for cooperative driving hardware simulation.

Generalized Haar Filter-Based Object Detection for Car Sharing Services

Object detection is important in car sharing services. Accuracy, efficiency, and low memory consumption are desirable for object detection in car sharing services. This paper presents a network system that satisfies all these requirements. Our approach first divides the object detection task into multiple simpler local regression tasks. Then, we propose the generalized Haar filter-based convolutional neural network to reduce the consumption of memory and computing resource. To achieve real-time performance, we introduce a sparse window generation strategy to reduce the number of input image patches without sacrificing accuracy. We perform experiments on both vehicle and pedestrian data sets. Experimental results demonstrate that our approach can accurately detect objects under challenging conditions. Note to Practitioners —Object detection is an important part of intelligent vehicle technologies, which play an important role in car sharing services. Object detection provides metadata for collision avoidance, self-driving systems, and driver-assistance systems, which can result in better safety and consumer experiences in car sharing services. Although deep learning has achieved an excellent performance in object detection, they consume a large amount of storage and computing resource, which makes them difficult to be deployed for car sharing services. This paper suggests a novel approach which is based on the generalized Haar filter and the local regression strategy. Our approach is accurate, efficient, and light. The experimental results verify the effectiveness of the proposed approach in car sharing services.

Autonomous lane changing control for intelligent vehicles

As an important content in intelligent vehicle technology, the design of lane changing path directly decides the quality of control in autonomous lane changing realization. This paper first summarizes the design principle of existing intelligent vehicle lane changing techniques. Then a novel lane changing path based on the X-Sin function is constructed. The X-Sin function is designed to combine the outstanding smoothness of sine function and the linear characteristic of the constant-speed shift function. With parameters including vehicle velocity, lane width and the maximum lateral acceleration, the proposed design solves the problems in lane changing path including jump point in lane changing path curvature and non-smooth lane changing path. A tracking controller is designed using quadratic optimum control to track the lane changing path. The effectiveness and steadiness of the proposed lane changing path is demonstrated by MATLAB simulation and real-vehicle experiments.