Intelligent Vehicle Technologies Research Articles

Research on Automatic Parking Systems Based on Parking Scene Recognition

As a key component of intelligent vehicle technology, automatic parking technology has become a popular research topic. Automatic parking technology can complete parking operations safely and quickly without a driver and can improve driving comfort, while greatly reducing the probability of parking accidents. An automatic parking system based on parking scene recognition is proposed in this paper to resolve the following issues with existing automatic parking systems: parking scene recognition methods are less intelligent, vehicle control has a low degree of automation, and the research scope is limited to traditional fuel vehicles. To increase the utilization of parking spaces and parking convenience, machine vision and pattern recognition techniques are introduced to intelligently recognize a vertical parking scenario, plan a reasonable parking path, develop a path tracking control strategy to improve the vehicle control automation, and explore a highly intelligent automatic parking technology roadmap. This paper gives three key aspects of system solutions for an automatic parking system based on parking scene recognition: parking scene recognition, parking path planning, and path tracking and control.

Design and research of automobile anti-collision warning system based on monocular vision sensor with license plate cooperative target

At present, detection method for the target vehicle based on monocular vision sensor uses the whole vehicle as targets. The automobile anti-collision technology proposed in this paper adopts monocular vision sensor for automobile measurement based on vehicle license plate cooperative target. Monocular vision sensor has advantages of good real-time performance and low cost. The technique can improve the detection capability of vehicle collision avoidance warning systems. In addition to the target vehicle positioning, it can also realize attitude determination. This technology eliminates the limits of road surface roughness and fluctuation. This paper designs the realization scheme of collision warning system based on monocular vision sensor from the automobile license plate cooperative target. Technology roadmap of automobile collision warning system is given. In this paper, license plate frame location is as the research background. The paper presents an analytic solution of positioning method for the license plate frame image. The method uses four vertex characteristics of license plate frame image to locate. Positioning speed of the method is fast. And it has a unique solution. This method can be used to positioning for the license plate frame. Simulation experiment is done for the collision warning location. The simulation results show that this method can locate the position for license plate frame image. License plate is regular shape, uniform, with identity recognition function markers on the automobile body. In the previous research on automotive collision warning and intelligent vehicle, we have not seen the research methods similar to the method introduced in this paper. The research enriches automobile anti-collision technology and theory of intelligent vehicle technology. It can also provide an auxiliary method for navigation and obstacle avoidance research for unmanned vehicle. It has certain scientific significance. Vehicle collision warning system can help the driver judgment, prompting warning, improving driving safety, and has broad application prospects.

Vulnerabilities of Android OS-Based Telematics System

Intelligent vehicle technologies have been developed rapidly. Modern vehicles include many Electronic Control Units (ECUs) and in-vehicle networks. While these technologies offer accurate vehicle control and increase the convenience and safety of drivers, their vulnerabilities also have been analyzed and exploited. Nevertheless, open platforms, such as the Android OS, have been introduced into vehicle systems without careful consideration about security issues. In this paper, we indicate the security problems of an Android OS-based telematics system. Our target device's firmware is offered on a public Web site and is easily analyzed using public analysis tools. This means that our analysis methods are more scalable and practical than previous ones for remote attacks that require difficult analysis skills, such as signal processing and reverse engineering. We also found that the device allows malicious firmware to be updated because of a problem related to misuse of certificates. Furthermore, we conducted attack experiments using a real vehicle.

Toward Next Active Safety Technology of Intelligent Vehicle

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/02.jpg"" width=""300"" /> Intelligent vehicle technologies</div>Autonomous driving has attracted attention in recent years from the viewpoint of energy consumption and traffic accident prevention; hence, its introduction has been desired. In Japan, various accident prevention safety technologies were developed for cooperative control between the driver and the vehicle system. For example, “adaptive cruise control system” and “lane departure warning system” were developed in the 1990s and “lane keeping assist system” and “braking control device for reducing collision damage” in the early stages of the 2000s. Later in Europe, autonomous driving systems were actively studied, and an automated braking system to avoid collisions was introduced in the market in the second half of the 2000s. Studies and development have been promoted for the practical use of active safety technologies based on autonomous driving technologies. Autonomous driving technologies could be applied to various cases, such as convoy travelling to compensate for the insufficient number of professional drivers or to improve their work environment, last-one-mile travelling from a public transportation station to home for the elderly, people who have children and people who need assistance, dead-man system for sudden illness of the driver, and automated parking for assisting the driver who is not good at it, or for the parking space to be effectively used. In this paper, an overview of the transition and history of vehicular technologies for safety and reliability is given. In particular, active safety technologies for traffic accident prevention and the necessary related technology trend are reviewed, and future problems are pointed out.

Special Issue on Intelligent Vehicle Technologies for Safety and Active Life

Intelligent automobiles equipped with active safety methods whose objective is to reduce accidents caused by human problems such as careless driving and loss of consciousness, have got the social acceptance widely which are recognized by introducing collision avoidance brakes to the market. Active safety and automated driving technologies of intelligent automobiles were featured at TokyoMotor Show 2015. Their displays attracted much attention.It is necessary to propose new technologies continuously by the technical subject of active safety and automated driving, for example, relation of cooperative driving by a vehicle system and a driver, from the restrictions of originality of technical developments and the liability issues at the time of an accident.Even so, users worldwide expect much from the dynamic future of transportation for safety and active life.This special issue features seven papers carefully reviewed by field specialists.I would like to express my heartfelt appreciation to the authors who have contributed their valuable research results to this special issue and to the reviewers who provided their invaluable expertise.I would also like to thank members of the Journal of Robotics and Mechatronics board for giving me the unique opportunity to plan and coordinate this issue.

Dynamic Prediction of Vehicle Cluster Distribution in Mixed Traffic: A Statistical Mechanics-Inspired Method

The advent of intelligent vehicle technologies holds significant potential to alter the dynamics of traffic flow. Prior work on the effects of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant information about the distribution of vehicle cluster size. Since the absence of large clusters could be offset by the presence of several smaller clusters, the distribution of cluster sizes can be as important as the presence or absence of clusters. To obtain a prediction of vehicle cluster distribution, the included work presents a statistical mechanics-inspired method of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control ( acc )-enabled vehicles exhibit a higher probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of large-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer large localized clusters as opposed to several smaller clusters spread across different locations on a highway.

Green vehicle technology to enhance the performance of a European port: A simulation model with a cost-benefit approach

In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port’s performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs’ ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one.

Curved Lane Detection using Improved Hough Transform and CLAHE in a Multi-Channel ROI

With the increase in advent of intelligent vehicle technology, LDWS technique is actively developing to provide assistance to drivers in generating warning signal in case of lane departure. In this paper, the input image is divided into four regions of interest. Then the CLAHE is applied on the given ROI for better image contrast and removal of noise using gradient filter. Improved Hough Transform is applied on the given image for the detection of curved and straight lanes. After detecting the lanes, they are colored with green color so that they are clearly visible. The proposed algorithm is analysed based on the parameters like Accuracy, BER, Fmeasure, and Sensitivity. The analysis shows that this algorithm provides efficient and effective performance compared to the existing algorithm.

Research on Stereo Matching Algorithm in Intelligent Vehicle Application

Perception of environment in front of driving vehicle is a core investigation theme of intelligent vehicle technologies aiming to increase safety, convenience and efficiency of driving. Using stereo vision for environment perception is a hot technology. This paper developed an algorithm for stereo matching in intelligent vehicle application. The experimental results indicate that this algorithm is effective. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

A Fast Forward Vehicle Detection Method by Multiple Vision Clues Fusion

With the development of intelligent vehicle technology, vehicle detection based on vision analysis has become an research hotspot in forward collision warning system development. Aiming to solve the existing problems in the current vehicle detection methods, for example, the detection accuracy is sensitive to the variation of illumination and object angle, we propose a forward moving vehicle detection method according to multiple vision clues fusion. Firstly, we locate the rough position using vehicle bottom shadow detection. The shadow is detected using an adaptive threshold image segmentation approach twice. Secondly, the symmetry of vehicle body and the perspective of camera field of view are both referenced to remove the inaccurate location in the first stage. The proposed method has been tested on several videos recorded in real urban conditions. Experimental results show that our method achieves 93.67% average detection accuracy in daytime, and its processing speed is more than 25fps. The proposed method has certain application prospects for improving the vision based forward collision warning system performance.

퍼지 논리와 Interacting Multiple Model (IMM)을 통한 잡음환경에서의 맞은편 차량의 중앙선 침범 예측

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

지능형 자동차 내부 네트워크에서 생체인증을 이용한 인증기법

현재 IT기술과 자동차기술을 융합한 지능형 자동차에 대한 연구가 활발히 진행중에 있으며 많은 새로운 서비스 모델들이 개발중에 있다. 지능형자동차에 대한 개발이 활발하게 이루어지면서 무선 네트워크를 이용한 다양한 서비스가 제공이 되고 있다. 이러한 지능형 자동차가 무선 네트워크를 이용한 서비스가 제동되면서 다양한 보안위협 요소가 도출되고 있다. 본 논문에서는 지능형 자동차의 외부에서 네트워크에 침입하여 지능형자동차의 보안위협 요소를 분석하고 지능형 자동차의 보안 솔루션의 인증 모델에 대한 기법을 제공하고자 한다. 인증 모델의 경우 신체의 고유성을 식별할 수 있는 생체인식을 이용한 인증기법을 제안한다. Studies on the intelligent vehicles that are fused with IT and intelligent vehicle technologies are currently under active discussion. And many new service models for them are being developed. As intelligent vehicles are being actively developed, a variety of wireless services are support. As such intelligent vehicles use wireless network, they are exposed to the diverse sources of security risk. This paper aims to examine the factors to threaten intelligent vehicle, which are usually intruded through network system and propose the security solution using biometric authentication technique. The proposed security system employs biometric authentication technique model that can distinguish the physical characteristics of user.

Research on Vehicle Automatically Tracking Mechanism in VANET

The intelligent vehicle is a complex system equipped with advanced technologies such as the artificial intelligence, automatic control, and computer, communication. It is a combination of multiple academic subjects and the latest technologies representing the developing tendency of future automobile technology and attracts more and more attention. In this paper, we made some useful explorations in the fields of intelligent vehicle control technology and obstacle avoidance, and a deep research is carried out about the distance of vehicle, vehicle tracking, vehicle lane changing and intersections obstacle avoidance and communication protocols, and some innovative ideas are proposed during the research. By using VANET, some predictable status of tracing and tracking of intelligent vehicle technology was researched, and the communication protocols between two vehicles, the safe spacing algorithm, the method of computing the actual distance at corners and straights, and the trajectory of lane change were designed. In addition, a series of vehicle tracing and tracking technologies, such as without knowing the road conditions, homeostatic mechanism, keeping a safe spacing to the target vehicle, and adjusting its own speed to track the target vehicle smoothly to the destination by comparing the actual distance and safe spacing between vehicles, were discussed here.

Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions

This paper examines the potential effectiveness of the following three precollision system (PCS) algorithms: 1) forward collision warning only; 2) forward collision warning and precrash brake assist; and 3) forward collision warning, precrash brake assist, and autonomous precrash brake. Real-world rear-end crashes were extracted from a nationally representative sample of collisions in the United States. A sample of 1396 collisions, corresponding to 1.1 million crashes, were computationally simulated as if they occurred, with the driver operating a precollision-system-equipped vehicle. A probability-based framework was developed to account for the variable driver reaction to the warning system. As more components were added to the algorithms, greater benefits were realized. The results indicate that the exemplar PCS investigated in this paper could reduce the severity (i.e., ΔV) of the collision between 14% and 34%. The number of moderately to fatally injured drivers who wore their seat belts could have been reduced by 29% to 50%. These collision-mitigating algorithms could have prevented 3.2% to 7.7% of rear-end collisions. This paper shows the dramatic reductions in serious and fatal injuries that a PCS, which is one of the first intelligent vehicle technologies to be deployed in production cars, can bring to highway safety when available throughout the fleet. This paper also presents the framework of an innovative safety benefits methodology that, when adapted to other emerging active safety technologies, can be employed to estimate potential reductions in the frequency and severity of highway crashes.

Floating Car Data Augmentation Based on Infrastructure Sensors and Neural Networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\hbox{CO}_{2}$</tex></formula> emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information from static points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information from floating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

Analysis of Distance Measurement System of Leading Vehicle

Based on the digital image processing theory, a new method of measuring the leading vehicle distance was proposed. The input image using the method of edge enhancement and morphological transformation was established, so the edges of objects were enhanced to identify. The target vehicle was identified and calibrated in the image by using the method of the obstacle detection by segmentation and decision tree. The relationship between coordinates value in image space and the data of the real space plane was established by applying the ray angles. Thus, through accessing to image pixel coordinates of the vehicle, the vehicle's actual position in the plane can be calculated. At last, the leading vehicle distance based on the calculating model of inverse perspective mapping was measured. By using software VC++, an experiment program was made. The experiment results prove that the method of measuring the leading vehicle distance is simple and effective. It can meet the requirement of intelligent vehicle technologies. It is an more available and more advanced method to calculate the leading vehicle distance. The target vehicle was identified and calibrated in the image by using the method of the obstacle detection by segmentation and decision tree. The relationship between coordinates value in image space and the data of the real space.

On-road assessment of in-vehicle driving workload for older drivers: Design guidelines for intelligent vehicles

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs), that offer a significant enhancement of safety and convenience to drivers and passengers. However, the use of ADAS- and IVIS-based information devices may increase driver distraction and workload, which in turn can increase the chance of traffic accidents. The number of traffic accidents involving older drivers that are due to distraction, misjudgment, and delayed detection of danger, all of which are related to the drivers’ declining physical and cognitive capabilities, has increased. Because the death rate in traffic accidents is higher when older drivers are involved, finding ways to reduce the distraction and workload of older drivers is important. This paper generalizes driver information device operations and assesses the workload while driving by means of experiments involving 40 drivers in real cars under actual road conditions. Five driving tasks (manual only, manual primarily, visual only, visual primarily, and visual-manual) and three age groups (younger (20–29 years of age), middle-aged (40–49 years of age), and older (60–69 years of age)) were considered in investigating the effect of age-related workload difference. Data were collected from 40 drivers who drove in a real car under actual road conditions. The experimental results showed that age influences driver workload while performing in-vehicle tasks.

실시간 운전 특성 모니터링 시스템을 위한 차량 환경 개발

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs) that offer a significant enhancement of safety and convenience to drivers and passengers. However, unsuitable design of HMI (Human Machine Interface) must increase driver distraction and workload, which in turn increase the chance of traffic accidents. Distraction in particular often occurs under a heavy driving workload due to multitasking with various electronic devices like a cell phone or a navigation system while driving. According to the 2005 road traffic accidents in Korea report published by the Road Traffic Authority (ROTA), more than 60% of the traffic accidents are related to driver error caused by distraction. This paper suggests the structure of vehicle environment for real-time driving behavior monitoring system while driving which is can be used the driver workload management systems (DWMS). On-road experiment results showed the feasibility of the suggested vehicle environment for driving behavior monitoring system.

에지특징의 단계적 조합과 수평대칭성에 기반한 선행차량검출

This paper presents an algorithm capable of detecting leading vehicles using a forward-looking camera. In fact, the accurate measurements of the contact locations of vehicles with road surface are prerequisites for the intelligent vehicle technologies based on a monocular vision. Relying on multistage processing of relevant edge features to the hypothesis generation of a vehicle, the proposed algorithm creates candidate positions being the left and right boundaries of vehicles, and searches for pairs to be vehicle boundaries from the potential positions by evaluating horizontal symmetry. The proposed algorithm is proven to be successful by experiments performed on images acquired by a moving vehicle.

Intelligent Vehicle Technology and Trends - [Book review

Intelligent Vehicle Technology and Trends - [Book review