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.

Inter-vehicle collision avoidance system

Abstract: In one embodiment, a controller of a local vehicle receives data from a remote vehicle that indicates a location and direction of travel of a remote vehicle. The controller determines a roadway path of travel of the remote vehicle using map data and the data received from the remote vehicle. The controller determines a roadway path of travel of the local vehicle using the map data and data indicative of a location and direction of travel of the local vehicle. The controller also determines whether the roadway path of travel of the local vehicle and the roadway path of travel of the remote vehicle intersect. The controller further provides an alert, in response to determining that the roadway path of travel of the local vehicle and the roadway path of travel of the remote vehicle intersect.

Collision avoidance system utilizing machine vision taillight tracking

Abstract: A vehicle-mounted sensing method and apparatus capable of monitoring the relative speed, distance, and closure rate between a sensor-equipped host vehicle and a sensed target object. The sensor uses an electronic camera (800) to passively collect information and to provide the information to a system (802) that identifies objects of interest using visual clues such as color, shape, and symmetry. The object's proximity may be determined, to a first approximation, by taking advantage of symmetrical relationships inherent in the vehicle of interest. The method and apparatus are particularly well-suited vehicular safety systems to provide for optimal risk assessment and deployment of multiple safety systems (804).

An Animal Detection and Collision Avoidance System Using Deep Learning

Abstract: All over the world, injuries and deaths of wildlife and humans are increasing day by day due to the huge road accidents. Thus, animal–vehicle collision (AVC) has been a significant threat for road safety including wildlife species. A mitigation measure needs to be taken to reduce the number of collisions between vehicles and wildlife animals for the road safety and conservation of wildlife. This paper proposes a novel animal detection and collision avoidance system using object detection technique. The proposed method considers neural network architecture like SSD and faster R-CNN for detection of animals. In this work, a new dataset is developed by considering 25 classes of various animals which contains 31,774 images. Then, an animal detection model based on SSD and faster R-CNN object detection is designed. The achievement of the proposed and existing method is evaluated by considering the criteria namely mean average precision (mAP) and detection speed. The mAP and detection speed of the proposed method are 80.5% at 100 fps and 82.11% at 10 fps for SSD and faster R-CNN, respectively.

Development of collision avoidance systems at delphi automotive systems

Abstract: This paper describes a systems engineering approach to the design and development of collision avoidance systems. It discusses the evolution of a collision avoidance system, and identifies the first component as an adaptive cruise control (ACC) system. This is then followed by a discussion regarding the need for data acquisition systems (DAS).

LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System

Abstract: Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quarantined by mapping (e.g., GPS) or via beacons that delineate a no-entry area. We propose a delineation method where the industrial vehicle utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to detect passive beacons and model-predictive control to stop the vehicle from entering a restricted space. The beacons are standard orange traffic cones with a highly reflective vertical pole attached. The LiDAR can readily detect these beacons, but suffers from false positives due to other reflective surfaces such as worker safety vests. Herein, we put forth a method for reducing false positive detection from the LiDAR by projecting the beacons in the camera imagery via a deep learning method and validating the detection using a neural network-learned projection from the camera to the LiDAR space. Experimental data collected at Mississippi State University's Center for Advanced Vehicular Systems (CAVS) shows the effectiveness of the proposed system in keeping the true detection while mitigating false positives.