Collision avoidance system

About: Collision avoidance system is a research topic. Over the lifetime, 1788 publications have been published within this topic receiving 23667 citations.

Midair collision and avoidance system (MCAS)

Abstract: A midair collision avoidance system (MCAS) employs an existing design of Traffic Alert and Collision Avoidance System (TCAS) as a module and seamlessly integrates it with a customized tactical module which is capable of providing unique tactical avoidance guidance control and display. The tactical module handles all phases of a tactical mission, including formation flight (e.g., formation fall-in, arming formation flight, engaging formation flight following, and formation break-away), and an air-refueling sequence (e.g., rendezvous, link-up, re-fueling, and disengaging air-refueling). The tactical module divides the air space around the aircraft into advisory, caution, and warning zones and for each provides display, tone and voice alerts to facilitate pop-up avoidance guidance commands. Military aircraft can thus effectively avoid mid air and near mid air collision situations in all three different operation modes: air traffic control (ATC) management mode, tactical mode, and a mixed mode.

Cooperative Intersection Collision Avoidance System (CICAS): Signalized Left Turn Assist and Traffic Signal Adaptation

Abstract: The Cooperative Intersection Collision Avoidance (CICAS) program is a multi-year, cooperative research program with federal, state, academic, and industry partners The CICAS-SLTA (Signalized Left Turn Assist) segment aims to address crashes caused by vehicles making left turns at signalized intersections that have no protected left-turn signal This report addresses so-called Left Turn Across Path-Opposite (LTAP/OD) crashes, which account for more than 27 percent of all US intersection-related crashes It describes direction problems, where drivers have cognitive, time pressure, or obstruction challenges making safe turn decisions with a permitted signalized left turn In addition, the report relates this research to other US DOT Vehicle-Infrastructure Integration (VII) activities The final goal is to show the feasibility of providing left-turn support and warnings to the driver through an in-vehicle interface

Vision based rail track and switch recognition for self-localization of trains in a rail network

Abstract: A collision avoidance system for railroad vehicles needs to determine their location in the railroad network precisely and reliably. For a vehicle-based system, that is independent from the infrastructure, it is vital to determine the direction a railroad vehicle turns at switches. In this paper a vision based approach is presented that allows to achieve this reliably, even under difficult conditions. In the images of a camera that observes the area in front of a railroad vehicle the rail tracks are detected in real-time. From the perspective of the moving railroad vehicle rail tracks branch and join from/to the currently travelled rail track. By tracking these rail tracks in the images, switches are detected as they are passed. It is shown that the followed track can be determined at branching switches. The approach is tested with real data from test rides in different locations and under a variety of weather conditions and environments. It proved to be very robust and of high practical use for track-selective self-localization of railroad vehicles, mandatory for collision avoidance.

Automatic collision avoidance system for ships using fuzzy control

Abstract: An automatic collision avoidance system is proposed. The system contains parts for data acquisition from the radar signal; decision support in relation to the collision risk; speed or course change command; and a course control autopilot. All parts except data acquisition are based on fuzzy control, which is quite similar to human control and flexible enough to adapt to unexpected environmental changes.

Multiple sensor collision avoidance system for automotive applications using an IMM approach for obstacle tracking

Abstract: In this paper a multi-sensor collision avoidance system is presented for automotive applications. For obstacle detection and tracking, millimeter wave (MMW) radar and a far infrared (FIR) camera are chosen in order to provide object lists to the sensors' trackers respectively. The algorithm for track management, data association, filtering and prediction for both sensors is also presented, focusing on Kalman filtering. Thus, an interacting multiple model (IMM) filter is designed for coping with all possible modes in which a car may be. Finally, a distributed fusion architecture using a central track file for the objects' tracks is adopted and described analytically. The results of the work will be used, among others, in the European co-funded project "EUCLIDE".