Sam-Yong Kim

Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion

Active research into vehicle detection and tracking using a vision sensor are done for driver assistance systems (DAS) - collision warning and avoidance, vision enhancement, etc. The vehicle detection and tracking algorithm for DAS requires a robust feature extraction and tracking method regardless of the light and road conditions and an exact estimation of vehicle position and velocity regardless of the distance from the ego-vehicle. But most research was carried out in the day time with good lighting conditions and the little research done so far in the night time assumed no interference of headlights from other vehicles. This paper proposes a new robust vehicle detection and tracking method regardless of the light and road conditions at any distance using vision and sonar sensors. We use the sonar sensor for detection and distance estimation within 10 m and the image sensor over 10 m. First, this paper proposes a simple method that can determine the light condition by observing several images and this light condition is used by selecting one of several detection methods. The proposed vehicle detection method in the day time image can extract the shadow region represented by the boundary between a vehicle and the road and further verify using other vehicle features, such as symmetry rate, vertical edge, and lane information. The vehicle tracking method in the day time uses online template matching using the mean image created by several consecutive detection results. The vehicle detection method in the night time extracts bright regions caused by the headlights, taillights, brake lights, etc. and these candidates are verified by observing several consecutive frames.

An Intelligent and Integrated Driver Assistance System for Increased Safety and Convenience Based on All-around Sensing

Advanced driver assistance systems (ADAS) support the driver’s decision making to increase safety and comfort by providing an ergonomic display of the driving environment as well as issuing the warning signals or even exerting active control in case of dangerous conditions. Most previous research and products intend to offer only a single warning service, such as lane departure warning, collision warning, lane change assistance, etc. Although each component of these functions elevates the driving safety and convenience to a certain degree, a new type of ADAS will be developed to integrate all of the important functions with an efficient human–machine interface (HMI) framework for various driving conditions. We present an all-around sensing system based on an integrated ADAS that senses all directions using 2 cameras and 8 sonars, recognizes the driving environment via lane and vehicle detection, and construct a novel bird’s-eye view HMI of the environment for easy comprehension that even gives a proper warning signal in case of imminent danger. It was tested on our experimental vehicle with a good demonstration its working. Further, it has a good potential for commercial use by virtue of the low cost of the sensors used.

A driver adaptive lane departure warning system based on image processing and a fuzzy evolutionary technique

By applying fixed threshold values for a single lane departure information such as lateral offset, most lane departure warning systems have a difficulty in practical application. This paper proposes a method that can generate the driver adaptive lane departure warning model that can reduce driver disturbance factors, such as a frequently annoying alarm, etc. After a training period, the lane departure warning model is constructed by a fuzzy-evolutionary algorithm that can fuse the current and near future vehicle state information like the lateral offset and TLC (Time to Lane Crossing). After the departure model has been constructed; the driver merely selects an appropriate hazard level of lane departure warning. The proposed system has been developed and tested in HiLS (hardware in the loop simulation).

Robust Automatic Parking without Odometry Using Enhanced Fuzzy Logic Controller

This paper develops a novel automatic parking algorithm based on a fuzzy logic controller with the vehicle pose for the input and the steering rate for the output. It localizes the vehicle by using only external sensors -a vision sensor and ultrasonic sensors. Then it automatically learns an optimal fuzzy if-then rule set from training data. This is possible using a genetic fuzzy system which optimizes the parameters for the fuzzy logic controller. Furthermore, it also finds the green zone of the ready-to-reverse position where parking is possible just by reversing. It has been tested on a Pioneer mobile robot which emulates the real vehicle.

Pose invariant face recognition with 3D morphable model and neural network

This paper introduces a pose invariant face recognition method with a training image and a query image using 3D morphable model and neural network. Our system uses 3D morphable model to get the reconstructed 3D face from the training image and obtains 2D image patches of facial components from the 3D face under varying head pose. The 2D image patches are used to train a neural network for pose invariant face recognition. Because those patches are obtained from the varying head pose, the neural network has robustness in the query image under the different head pose form the training image. Our pose invariant face recognition system has the performance of correct recognition higher than 98% with BJUT 3D scan database.

Geometrical interpretation of fuzzy data and shape detection

In this paper, we present a method that can be used in detecting parameters or shapes from a set of given data. Each datum is interpreted as fuzzy in the geometrical sense. This method enables us to detect more than one shape from a set of data. It also reduces the high memory requirement problem which is one of the disadvantages of the Hough transform. The problem is formulated as an optimization problem using a simple genetic algorithm. Even though this method can be applied, conceptually, to any nonlinear problem, we only show simple linear cases. Conceptual derivation and experimental results of our method are shown in this paper.