The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a decision-making framework for automated driving in highway environments. The framework is capable of reliably, robustly assessing a given highway situation (with respect to the possibility of collision) and of automatically determining an appropriate maneuver for the situation. It consists of two main components: situation assessment and strategy decision. The situation assessment...
This paper presents a risk assessment algorithm for automatic lane change maneuvers on highways. It is capable of reliably assessing a given highway situation in terms of the possibility of collisions and robustly giving a recommendation for lane changes. The algorithm infers potential collision risks of observed vehicles based on Bayesian networks considering uncertainties of its input data. It utilizes...
This paper presents a cooperative system by vehicle-to-infrastructure (V2I) communications that extends the range of environmental perception and improves the performance of situation awareness for highly automated driving. The paper consists of two steps: data fusion based situation awareness and distributed reasoning based situation assessment. The data fusion produces a V2I augmented map to provide...
A primary challenge of automated driving systems is the task of a situation assessment. This paper presents a high-level data fusion based probabilistic situation assessment method which is capable of assessing a current traffic situation and giving a recommendation about driving behaviors. The proposed method consists of two steps: high-level data fusion and probabilistic situation assessment. The...
This paper proposes an automated system with respect to situation assessment and behavior decision not only for cooperative driving between a driver and the system but also highly automated driving in highway environments. The proposed system includes three main parts: (1) high-level data fusion to produce a better understanding of the observed situation, (2) distributed reasoning based situation...
Collision avoidance is an essential part in autonomous navigation. This paper proposes a collision avoidance method in on-road environment for autonomous driving. The proposed method divides a road map into six lane-level regions, assigns risk observers to corresponding regions, evaluates collision risks of situations based on risk observer distribution, and determines behaviors to deal with various...
We study the Chi-Squared (χ2) distance and metric learning as a problem of Large Margin Nearest Neighbor (LMNN) classification. We suggest the χ2 metric learning algorithm, based on the LMNN approach, to learn a metric to improve the accuracy of k-nearest neighbor (kNN) classification. We show that the χ2 distance in the transformed space is one of the Quadratic-Chi distance family members. We use...
This paper describes a development of bio-mimetic robot hands and its control scheme. Each robot hand has four under-actuated fingers, which are driven by two linear actuators coupled. According to the study of the human hand, it is noted that coupled muscles (flexor and extensor muscle) generate the finger motion. Each fingertip can reach toward objects by curved surface workspace in 3D-space. The...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.