Collision avoidance

About: Collision avoidance is a research topic. Over the lifetime, 8014 publications have been published within this topic receiving 111414 citations.

Sampling-Based Path Planning for UAV Collision Avoidance

Abstract: The ability to avoid collisions with moving obstacles, such as commercial aircraft is critical to the safe operation of unmanned aerial vehicles (UAVs) and other air traffic. This paper presents the design and implementation of sampling-based path planning methods for a UAV to avoid collision with commercial aircraft and other moving obstacles. In detail, the authors develop and demonstrate a method based on the closed-loop rapidly-exploring random tree algorithm and three variations of it. The variations are: 1) simplification of trajectory generation strategy; 2) utilization of intermediate waypoints; 3) collision prediction using reachable set. The methods were validated in software-in-the-loop simulations, hardware-in-the-loop simulations, and real flight experiments. It is shown that the algorithms are able to generate collision free paths in real time for the different types of UAVs among moving obstacles of different numbers, approaching angles, and speeds.

Obstacle Detection and Collision Avoidance for a UAV With Complementary Low-Cost Sensors

Abstract: This paper demonstrates an innovative and simple solution for obstacle detection and collision avoidance of unmanned aerial vehicles (UAVs) optimized for and evaluated with quadrotors. The sensors exploited in this paper are low-cost ultrasonic and infrared range finders, which are much cheaper though noisier than more expensive sensors such as laser scanners. This needs to be taken into consideration for the design, implementation, and parametrization of the signal processing and control algorithm for such a system, which is the topic of this paper. For improved data fusion, inertial and optical flow sensors are used as a distance derivative for reference. As a result, a UAV is capable of distance controlled collision avoidance, which is more complex and powerful than comparable simple solutions. At the same time, the solution remains simple with a low computational burden. Thus, memory and time-consuming simultaneous localization and mapping is not required for collision avoidance.

Collision Avoidance Methods and Systems

Abstract: Method for avoiding collisions between a host vehicle and other vehicles in which the position of the vehicles is determined, the vehicles are equipped with a transmitter/receiver, and in the host vehicle, the possibility of a collision involving the host vehicle is assessed by receiving signals from the transmitter/receivers of each other vehicle, analyzing the received signals to extract positional information about the transmitter/receivers from each signal, and when a received signal contains additional information of interest about a possible collision involving the host vehicle, analyzing the extracted positional information to determine whether any signals contain additional information of interest about a possible collision involving the host vehicle. Additional information is extracted only from such signals and analyzed to ascertain whether a collision between the host vehicle and any other vehicles is likely to occur in order to enable action to be taken to prevent the collision, e.g., evasive action.

Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios

Abstract: The autonomous emergency brake (AEB) is an active safety function for vehicles which aims to reduce the severity of a collision. An AEB performs a full brake when an accident becomes unavoidable. Even if this system cannot, in general, avoid the accident, it reduces the energy of the crash impact and is therefore referred to as a collision mitigation system. A new approach for the calculation of the trigger time of an emergency brake will be presented. The algorithm simultaneously considers all physically possible trajectories of the object and host vehicle. It can be applied to all different scenarios including rear-end collisions, collisions at intersections, and collisions with oncoming vehicles. Thus, 63% of possible accidents are addressed. The approach accounts for the object and host vehicles' dimensions. Unlike previous work, the orientation of the vehicles is incorporated into the collision estimation.

Artificial potential functions for highway driving with collision avoidance

Abstract: We present a set of potential function components to assist an automated or semi-automated vehicle in navigating a multi-lane, populated highway. The resulting potential field is constructed as a superposition of disparate functions for lane- keeping, road-staying, speed preference, and vehicle avoidance and passing. The construction of the vehicle avoidance potential is of primary importance, incorporating the structure and protocol of laned highway driving. Particularly, the shape and dimensions of the potential field behind each obstacle vehicle can appropriately encourage control vehicle slowing and/or passing, depending on the cars' velocities and surrounding traffic. Hard barriers on roadway edges and soft boundaries between navigable lanes keep the vehicle on the highway, with a preference to travel in a lane center.