Hanwool Woo

Lane-Change Detection Based on Vehicle-Trajectory Prediction

We propose a new detection method to predict a vehicles trajectory and use it for detecting lane changes of surrounding vehicles. According to the previous research, more than 90% of the car crashes are caused by human errors, and lane changes are the main factor. Therefore, if a lane change can be detected before a vehicle crosses the centerline, accident rates will decrease. Previously reported detection methods have the problem of frequent false alarms caused by zigzag driving that can result in user distrust in driving safety support systems. Most cases of zigzag driving are caused by the abortion of a lane change due to the presence of adjacent vehicles on the next lane. Our approach reduces false alarms by considering the possibility of a crash with adjacent vehicles by applying trajectory prediction when the target vehicle attempts to change a lane, and it reflects the result of lane-change detection. We used a traffic dataset with more than 500 lane changes and confirmed that the proposed method can considerably improve the detection performance.

Lane-changing feature extraction using multisensor integration

We propose a feature extraction method for lane changes of other traffic participants. According to previous research, over 90 % of car crashes are caused by human mistakes, and lane changes are the main factor. Therefore, if an intelligent system can predict a lane change and alarm a driver before another vehicle crosses the center line, this can contribute to reducing the accident rate. The main contribution of this work is to propose a feature extraction method using the multisensor system which consists of a position sensor and a laser scanner with line markings information. For a lane change prediction of other traffic participants, the most effective features are a lateral position and velocity with respect to a center line. We installed the sensor system to the primary vehicle and measured positions of other traffic participants while the primary vehicle drives on a highway. We extracted the features as the distance with respect to the center line and the lateral velocity of other vehicles using the measurement data. We confirmed that our feature extraction method has an enough accuracy for the lane change prediction.

3D reconstruction of line features using multi-view acoustic images in underwater environment

In order to understand the underwater environment, it is essential to use sensing methodologies able to perceive the three dimensional (3D) information of the explored site. Sonar sensors are commonly employed in underwater exploration. This paper presents a novel methodology able to retrieve 3D information of underwater objects. The proposed solution employs an acoustic camera, which represents the next generation of sonar sensors, to extract and track the line of the underwater objects which are used as visual features for the image processing algorithm. In this work, we concentrate on artificial underwater environments, such as dams and bridges. In these structured environments, the line segments are preferred over the points feature, as they can represent structure information more effectively. We also developed a method for automatic extraction and correspondences matching of line features. Our approach enables 3D measurement of underwater objects using arbitrary viewpoints based on an extended Kalman filter (EKF). The probabilistic method allows computing the 3D reconstruction of underwater objects even in presence of uncertainty in the control input of the cameras movements. Experiments have been performed in real environments. Results showed the effectiveness and accuracy of the proposed solution.

Radiation Source Localization Using SLAM for Compton Camera Mounted on Mobile Robot

For taking out the melted down nuclear fuel debris in Fukushima nuclear power plant, localization of the debris inside plant is required. Because of high radioactive in Fukushima nuclear power plant, a mobile robot mounting the Compton camera is used in order to investigate the power plant instead of human. However, the mobile robot has uncertainties of its pose. We propose a method to localize radiation source and the robot using extended Kalman Filter (EKF)based simultaneous localization and mapping (SLAM) with Compton camera. We evaluated the performance of proposed method in a simulated environment.

Dynamic potential-model-based feature for lane change prediction

We propose a prediction method for lane changes in other vehicles. According to previous research, over 90 % of car crashes are caused by human mistakes, and lane changes are the main factor. Therefore, if an intelligent system can predict a lane change and alarm a driver before another vehicle crosses the center line, this can contribute to reducing the accident rate. The main contribution of this work is to propose a new feature describing the relationship of a vehicle to adjacent vehicles. We represent the new feature using a dynamic characteristic potential field that changes the distribution depending on the relative number of adjacent vehicles. The new feature addresses numerous situations in which lane changes are made. Adding the new feature can be expected to improve prediction performance. We trained the prediction model and evaluated the performance using a real traffic dataset with over 900 lane changes, and we confirmed that the proposed method outperforms previous methods in terms of both accuracy and prediction time.