Safe Driving Support System Research Articles

Detection of Road Images Containing a Counterlight Using Multilevel Analysis

In this paper, a method for detecting real-time images that include counterlight produced by the sun, is proposed. It involves applying a multistep analysis of the size, location, and distribution of bright areas in the image. In general, images containing counterlight have a symmetrically high brightness value at a specific location spread over an extremely large region. In addition, the distribution and change in brightness in that specific region have a symmetrically large difference compared with other regions. Through a multistep analysis of these symmetrical features, it is determined whether counterlight is included in the image. The proposed method presents a processing time of approximately 0.7 s and a detection accuracy of 88%, suggesting that the approach can be applied to a safe driving support system for autonomous vehicles.

Human-Friendly Safe Driving Support System of Electric Wheelchair for Drift Suppression

Human-Friendly Safe Driving Support System of Electric Wheelchair for Drift Suppression

차량속도 데이터를 이용한 안전 운전지원 시스템

본 논문에서는 차량의 운행 중에 발생하는 차량속도 데이터를 이용하여 운전자가 안전 운전을 할 수 있는 차량 안전운전을 위한 정보를 제공하는 시스템을 설계하였다. 시스템의 구성은 데이터 수집을 위한 단말기, 차량에서 수집된 정보를 서버에 전송하기 위한 게이트웨이 기능을 갖는 스마트 폰앱, 수집된 데이터를 활용하여 운전자의 안전을 위한 정보를 제공할 수 있는 알고리즘을 구현하는 서버로 구성된다. 안전운전을 위한 알고리즘에 대한 구현은 운전자의 정보, 도로에 대한 정보 및 단말기에서 수신된 차량속도를 이용하여 알고리즘을 구현하였다. 운전하는 도로의 위치 및 연령에 따라 수집된 차량속도 데이터를 이용한 실험결과, 도로구간별 속도제안 법률과 운전자 연령별 평균속도를 초과할 경우 본 논문에서 제안한 기법이 위험운전을 경고할 수 있다는 것을 확인하였다.

Influence of Safe Driving Support System for Electric Wheelchair on Operation Input

This paper describes the influence on operation input which is given by a safe driving support system for an electric wheelchair. A wheelchair is rapidly introduced as a mobility for elderly people who has disabilities on walking. Along with that, collision accidents of the wheelchair also increase and many causes are mistakes of operation and recognition of a driver. Therefore we developed the safe driving support system which contains a Laser Range Finder (LRF) and Ultrasonic Sensors (USSs) for obstacle detection. The wheelchair avoids the collision based on degrees of collision risk which is calculated based on fuzzy reasoning from control input of joystick and the distance for obstacles measured by the LRF and USSs. The system not only affects motion of the wheelchair, but also the driver’s condition and judgement for driving. We confirmed that the system enhances a controllability and safety, and influences operation input of driver from some experimental results.

Safe Driving Support System for Electric Wheelchair Based on Time to Collision and Degree of Danger

Safe Driving Support System for Electric Wheelchair Based on Time to Collision and Degree of Danger

Advanced Safety Vehicle (ASV) Technology Driver Support System Monitor using Three Onboard Camera

This paper presents the development of safe driving support system using three onboard cameras. One camera monitors the driver to determine the current attention location, and the other is a front camera that detects pedestrians, running lane and vehicles in front. The rear camera detects pedestrians and approaching vehicles. The pedestrians and vehicles are detected using a specially trained HOG/Adaboost system. Lane detections use edge detection and RANSAC. Information from the three cameras is then used to determine if a situation is dangerous enough to warrant warning the driver. We have conducted experiments, and the results confirm that this system has the potential to support automatic driving.

The Latest Safety Driving Support Technologies and Automotive Lighting Technology(<Feature>Lighting Coordinations of Road and Car for Our Goal of Further Traffic Safety)

In this paper, in the beginning, efforts of ITS aimed at reduction of traffic accidents are introduced. Then, it is commentary sprinkled with examples on the progress of the automobile society due to the spread of safe driving support system and automatic operation system and automobile and infrastructure of information exchange and ultra-compact mobility. It is important to support the view secure in operation driver for safe driving at night. In relation to development of headlamp technology for performing this support, examples of the development of the illumination source and the headlamp technology is introduced.

ECG 기반의 운전자별 인지 부하 평가 방법 개발

Objective: The present study is to develop an effective evaluation method for a driver’s cognitive workload using electrocardiography (ECG) signal. Background: ECG measures such as heart rate (HR) and heart rate variability (HRV) have been used for cognitive workload evaluation. Since ECG changes by cognitive workload vary largely depending on personal characteristics, an optimal analysis protocol of ECG needs to be tailored to each individual accordingly; however, existing studies have not considered personal characteristics in ECG analysis for cognitive workload evaluation. Method: The proposed evaluation method uses the area under the receiver operating characteristic (ROC) curve (AUC). A preliminary analysis was conducted with ECG data collected in a driving simulator while an n-back task was conducted. AUC analysis was performed for four ECG metrics (mean IBI, SDNN, RMSSD, and RMSE), three window spans (20, 30, and 40 seconds), and three update rates (1, 2, and 3 seconds). Results: It was identified that the optimal ECG analysis parameters of metric, window span, and update rate maximizing the discriminability of cognitive workload evaluation varied between individual drivers. Conclusion: The finding of the present study supports the use of an individually customized ECG analysis protocol for better evaluation accuracy of a drivers’ cognitive workload. Application: The proposed ECG analysis method for cognitive workload evaluation can be applied to development of a safe driving support system.

MIMO‐STBC scheme for intervehicle communications using decision feedback channel estimation method based on propagation feature at intersection

AbstractThis paper investigates an intervehicle communication method as a safe driving support system for intersection collision avoidance warnings. In this kind of system, realization of highly reliable communications in the physical layer is an absolute requirement. As a result, we propose a new MIMO‐STBC (Space‐Time Block Coding) method based on intersection propagation channel characteristics. MIMO transmission characteristics are determined by the propagation channel characteristics. First, we use a ray‐tracing method simulation to clarify the propagation channel conditions, and then model the intersection propagation channel. We use the characteristics of this propagation channel to propose a MIMO‐STBC method that utilizes decision feedback channel estimation, and verify the effectiveness of the proposed method in computer simulations. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 90(9): 1–15, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20400

Fundamental research about the effectiveness of a safe driving support system with information provision

Recently, safe driving support systems such as AHS-i have been proposed. In this paper, we investigate the relationship between the reliability of information provided from a safety system and the driver's unsafe action, and then quantitatively analyze the reliability level required for the system to reduce traffic accidents.

Millimeter Wave Radar Technology-Based Intelligent Winter Highway Systems

To help prevent accidents in winter, the Civil Engineering Research Institute of the Hokkaido Development Bureau has been promoting research and development on a safe-driving support system for winter roads. The focus is on millimeter wave radar as a promising technology for detecting obstacles ahead, such as stopped cars, when visibility is reduced by snowstorms. A questionnaire survey on winter roads found the safe-driving support system with millimeter wave radar to be effective for road sections in flat areas, tunnels, intersections, and expressway ramps. Therefore, verification tests were conducted on a test system. Millimeter wave radar effectively detected obstacles on roads where visibility was reduced. For enhanced detection accuracy, a combined millimeter wave radar and infrared camera was developed, and an obstacle detection test was carried out with the integrated system. Collation of data from the radar and the camera, each of which tracks the object independently, reduced the error. The effect of a warning of danger on driving behavior was tested. A reduction in the difference of driving behavior between drivers of different demographic categories was observed when information about the obstacle was provided. This indicates effectiveness in preventing accidents. Additional tests in which subjects were aided by video or computer graphic images confirmed the effectiveness of intelligent, light-emitting delineators in warning drivers.

A Hardware/Software Co-Design Method in Development of ITS Information Processing and Its Verification

In this article, a hardware and software co-design method for the image processing system of Intelligent Transport System (ITS) is proposed. The objective of this method is to optimize the division of functions between hardware and software by the optimum trade-off. Under this method, first, the targeted overall system is modeled by software to adjust functions and structures of the system. Then, the performance of each function of software and hardware in the model is evaluated by simulation, and the optimized division of functions between hardware and software is eventually obtained. Each function divided in hardware and software is designed in detail using development tools and then the results of the design of hardware and software are loaded on the co-design test board to verify and evaluate satisfaction in the design objectives. The ITS image processing development area is chosen as an application example for this method and its effectiveness is verified by evaluating the construction of a safe driving support system.