Insect-vision inspired collision warning vision processor for automobiles

Abstract

Vision is expected to play important roles for car safety enhancement. Imaging systems can be used to enlarging the vision field of the driver. For instance capturing and displaying views of hidden areas around the car which the driver can analyze for safer decision-making. Vision systems go a step further. They can autonomously analyze the visual information, identify dangerous situations and prompt the delivery of warning signals. For instance in case of road lane departure, if an overtaking car is in the blind spot, if an object is approaching within collision course, etc. Processing capabilities are also needed for applications viewing the car interior such as >intelligent airbag systems> that base deployment decisions on passenger features. On-line processing of visual information for car safety involves multiple sensors and views, huge amount of data per view and large frame rates. The associated computational load may be prohibitive for conventional processing architectures. Dedicated systems with embedded local processing capabilities may be needed to confront the challenges. This paper describes a dedicated sensory-processing architecture for collision warning which is inspired by insect vision. Particularly, the paper relies on the exploitation of the knowledge about the behavior of Locusta Migratoria to develop dedicated chips and systems which are integrated into model cars as well as into a commercial car (Volvo XC90) and tested to deliver collision warnings in real traffic scenarios.