Collision avoidance

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

AutonoVi: Autonomous vehicle planning with dynamic maneuvers and traffic constraints

Abstract: We present AutonoVi, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and integrates traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.

Maritime Collision Avoidance as a Differential Game

Abstract: Collision avoidance during a two-ship encounter in the open sea is treated as a problem of optimal control using the theory of differential games. Each ship is assumed to have two controls corresponding to rudder angle and engine setting. Different versions of the game are obtained. Optimal evasive maneuvers and limiting conditions for ship encounters beyond which collision becomes unavoidable are determined analytically. Numerical examples are given for encounters between two ships, one of which has a higher speed and the other a smaller turning radius. Results are presented in a graphical form suitable for display on a radar screen.

Model-Based Optimization of Airborne Collision Avoidance Logic

Abstract: : The Traffic Alert and Collision Avoidance System (TCAS) is designed to reduce the risk of mid-air collisions by providing resolution advisories to pilots. The current version of the collision avoidance logic was hand-crafted over the course of many years and contains many parameters that have been tuned to varying extents and heuristic rides whose justification has been lost. Further development of the TCAS system is required to make the system compatible with next generation air traffic control procedures and surveillance systems that will reduce separation between aircraft. This report presents a decision-theoretic approach to optimizing the TCAS logic using probabilistic models of aircraft behavior and a cost metric that balances the cost of alerting with the cost of collision. Such an approach has the potential for meeting or exceeding the current safety level while lowering the false alert rate and simplifying the process of re-optimizing the logic in response to changes in the airspace and sensor capabilities.