:: ICCAS 2015 ::

FP01		Autonomous Vehicles
Time : 16:30~18:00 Room : 201~204		Chair : (, )



	FP01-1

The Cognitive Driving Framework: Joint Inference for Collision Prediction and Avoidance in Autonomous Vehicles
Alan J Hamlet, Patrick O Emami, Carl D Crane(University of Florida, United States)

This paper describes a novel method for allowing an autonomous ground vehicle to predict the intent of other agents in an urban environment. This method, termed the cognitive driving framework, models both the intent and the potentially false beliefs of an obstacle vehicle. By modeling the relationships between these variables as a dynamic Bayesian network, filtering can be performed to calculate the intent of the obstacle vehicle as well as its belief about the environment. This joint knowledge can be exploited to plan safer and more efficient trajectories when navigating in an urban environm



	FP01-2

On-Road Vehicle Detection based on Appearance Features for Autonomous Vehicles
Tae young Lee, Jung ha Kim(University, Korea, Republic of )

In this paper, we propose a monocular camera-based vehicle detection system for use in autonomous vehicles. We have designed a vehicle detection system that follows two basic steps namely; Hypothesis Generation and Hypothesis Verification. In the HG step, a candidate region of vehicles is set by using the shadow properties of the vehicle. In the HV step, based on the candidate regions, we are able to distinguish between the vehicle and the non-vehicle. For the HV, we use histograms of oriented gradients (HOG) feature and support vector machine (SVM) classifier.



	FP01-3

Night time Vehicle Detection By Using Color Information Based on Tail-Light
ZHANG JUNJIE junger, Jung-Ha Kim(Kookmin Unmanned vehicle reserch Lab(KUL), Korea, Republic of )

In this paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of vehicle detection base on vision processing system. As a preprocessing for high speed vehicle detection in the night, the vanishing line estimation and the optimal extraction of ROI (region of interest) for can be processed to reduction of detection region in which high speed processing is enabled. In order to get better performance the image need pre-processing. Legally vehicle tail light must be red color, so we use the red plane image with simultaneous processing



	FP01-4

Lateral Control for Autonomous Lane Keeping System on Highways
Chang Mook Kang, Jeehyung Lee, Sung Gu Yi, Soo Jung Jeon(Hanyang University, Korea, Republic of ), Youngseop Son(MANDO corporation, Korea, Republic of ), Wonhee Kim(Dong-A University, Korea, Republic of ), Seung-Hi Lee, Chung Choo Chung(Hanyang University, Korea, Republic of )

In this paper, we propose a lateral control scheme for autonomous lane keeping system on highways. Three main techniques are proposed, to improve the lane-keeping performance and and to reduce the ripple in the yaw rate on highways. First, we propose a virtual lane prediction method to cope with the momentary failure of lane detection. Second, we innovate an approach to steering wheel angle control based on torque overlay for the EPS of the LKS. Finally, the multi-rate lane-keeping control scheme is proposed to improve the lane-keeping performance and to reduce the ripple in the yaw rate. The



	FP01-5

Estimation of the Maximum Lateral Tire-Road Friction Coefficient Using the 6-DoF Sensor
Kyoungseok Han, Eunjae Lee, Seibum Choi(KAIST, Korea, Republic of )

This paper provides the lateral tire-road friction coefficient estimation method using a simplified Dug-off tire model under the pure side slip condition. The rearranged Dugoff model respect to lateral friction coefficient consists of lateral tire force, normal tire force, wheel side slip angle and cornering stiffness. Therefore, aforementioned parameters are also identified based on vehicle model or it is assumed to be constant values. The performance of developed algorithm is verified by Carsim and Matlab/Simulink. The simulation results encourage possible development of this method



	FP01-6

Power Management Strategy of Hybrid Electric Vehicle using Power Split Ratio Line Control Strategy based on Dynamic Programming
Heeyun Lee, Sukwon Cha(Seoul National University, Korea, Republic of ), Yeong-il Park(Seoul National University of Science and Technology, Korea, Republic of )

This paper is concerned with power management strategy of hybrid electric vehicle. Dynamic programming based power split ratio line control strategy is studied for parallel type hybrid electric vehicle to improve fuel economy performance.On the assumption that future driving cycle is given, backward vehicle simulation with dynamic programming is used to extract information about optimal control policy from defined driving cycle information and the interpreted result is applied to parameter of power split ratio line control strategy.

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