• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.10
• /
• pp.803-810
• /
• 2016

This paper presents a study of localization methods based on particle filter using 2D laser sensor measurements and road feature map information, for autonomous vehicles. In order to navigate in an urban environment, an autonomous vehicle should be able to estimate the location of the ego-vehicle with reasonable accuracy. In this study, road features such as curbs and road markings are detected to construct a grid-based feature map using 2D laser range finder measurements. Then, we describe a particle filter-based method for accurate positional estimation of the autonomous vehicle in real-time. Finally, the performance of the proposed method is verified through real road driving experiments, in comparison with accurate DGPS data as a reference.

• Journal of Auto-vehicle Safety Association
• /
• v.5 no.2
• /
• pp.17-23
• /
• 2013

This paper describes an algorithm for Advanced Emergency Braking(AEB) with tire-road friction coefficient estimation. The AEB is a system to avoid a collision or mitigate a collision impact by decelerating the car automatically when forward collision is imminent. Typical AEB system is operated by Time-to-collision(TTC), which considers only relative velocity and clearance from control vehicle to preceding vehicle. AEB operation by TTC has a limit that tire-road friction coefficient is not considered. In this paper, Tire-road friction coefficient is also considered to achieve more safe operation of AEB. Interacting Multiple Model method(IMM) is used for Tire-road friction coefficient estimation. The AEB algorithm consists of friction coefficient estimator and upper level controller and lower level controller. The numerical simulation has been conducted to demonstrate the control performance of the proposed AEB algorithm. The simulation study has been conducted with a closed-loop driver-controller-vehicle system using using MATLAB-Simulink software and CarSim Vehicle model.

• Journal of Auto-vehicle Safety Association
• /
• v.10 no.1
• /
• pp.38-44
• /
• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.107.2-107
• /
• 2002

The objective of this study is to propose a lane changing and keeping method on a curved road for an automatic guidance of a vehicle. It is well known that the speed control of a vehicle in a curved road is essential in terms of vehicle stability and passenger safety because centrifugal force makes a vehicle to be on out of lane. And it is also natural to avoid the collision with other cars or obstructions with keeping the stability and drivability. The vehicle pose and the road curvature were calculated by geometrically fusing sensor data from camera image, tachometer and steering wheel encoder though the Perception Net in which not only the state variables, but also the corresponding uncer...

• Journal of the Korea Convergence Society
• /
• v.12 no.2
• /
• pp.1-6
• /
• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• KIPS Transactions on Computer and Communication Systems
• /
• v.2 no.6
• /
• pp.253-256
• /
• 2013

For the reliable delivery of safety-related information to vehicles in the VANET, a reliable VANET routing protocol is required. In this paper, we propose a routing protocol that works based on the road vehicle density information for fast and reliable communications among vehicles within the city environment VANET. In the proposed mechanism, each vehicle computes the road vehicle density by using beacon messages and the road information. Based on the road vehicle density information, each vehicle establishes a reliable route for packet delivery. Through the NS-2 based simulations, we compare our proposed mechanism with GPSR and show that our mechanism outperforms GPSR in terms of packet delivery success rate.

• International Journal of Highway Engineering
• /
• v.13 no.4
• /
• pp.167-175
• /
• 2011

Evaluation of fuel consumption for the various road condition and vehicle type is necessary to perform the economic analysis of road construction which is important for the efficient design and management of road. Economic analysis of road should consider the social cost which can be divided into agency cost including initial construction expense, maintenance cost, and so on, and user cost consisting of vehicle operating cost, congestion cost, etc. Since vehicle operating cost depends on the traffic volume, fuel consumption that is a major part of vehicle operating cost will change by traffic volume as well. Fuel consumption is significantly affected by vehicle speed and road condition, especially the roughness. Thus, fuel consumption should be evaluated in terms of road condition, which is not currently considered. In this study, the estimation model of fuel consumption for the passenger cars in Korea has been developed by considering the road condition. First, the relationship between vehicle speed and fuel consumption that is used to calculate the vehicle operating cost for investment evaluation of transportation facility and the initial feasibility study of road construction was investigated. Second, with the consideration of road roughness, fuel consumption of the passenger car was measured. From the measurement, it was found that fuel consumption increased by $80m{\ell}$ per 100km driving as the roughness increased by 1m/km. Therefore, it is recommended that for the economic analysis of road design and management, the fuel consumption should be a function of road roughness.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.7 no.9
• /
• pp.146-157
• /
• 1999

Safety systems for road vehicles have been rapidly developed in recent years. Especially, the VDC(Vehicle dynamics Control) system is a new active safety system for road vehicles which controls its dynamic vehicle motion in emergency situations . In the case of configuring the VDC system by utilizing the ABS(Anti-lock Brake System), the role of a control logic which directly influences the vehicle motion is very important. In this study the performance of the VDC vehicle was compared to the performances of the CBS (Conventional Brake system )and ABS vehicle. For various driving conditions , the simulation of vehicle dynamics with known VDC control logics was performed. Analysis results showed the VDC vehicle could stably perform even on the road of low coefficient of friction. In addition it was shown that the basic control logic for the VDC system could outstandingly improve driving stability in the case of braking as well as constant speed cruising.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.88-93
• /
• 1993

This paper introduces an ARV(Autonomous Road Vehicle) system which can run on orads without help of a driver by detecting road boundaries through computer vision. This vehicle can also detect obstacles in front through sonar sensors and infrared sensors. This system largely consists of a handle steering module and a braking module. From road boundaries, the steering module determines handle turn angle. The braking module stops or decelerates to avoid collision depending on the relative speeds and distance to the obstacles detected by different sensors. This ARV system has been implemented in a small jeep and can run 30-40 km/h city traffic. In this paper, we illustrate the structure of the ARV systems and its operation principle.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.5 no.1
• /
• pp.136-145
• /
• 1997

To analyzed ride quality and to predict durability in vehicle dynamics, it is essential to describe a road surface profile precisely. This paper presents a technique to generate road surface profiles in a spatial domain by using a power spectral density function. A single track power spectral density function is proposed to describe a road surface profile, which is also applicable for multi-track vehicle response analysis, The derived road surfaces are compared to ISO(International Organization for Standardization) standards and classifications, proposed by the MIRA(Motor Industry Research Association). The methodology in this paper is also proposed to generate road roughness description with a limited external data. A small amount of external curve data is combined with an internal PSD function to generate road surface roughness in a spatial domain.