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Technical Paper

Optimizing Color Detection with Robotic Vision Sensors for Lane Following and Traffic Sign Recognition in Small Scale Autonomous Test Vehicles

An important aspect of an autonomous vehicle system, aside from the crucial features of path following and obstacle detection, is the ability to accurately and effectively recognize visual cues present on the roads, such as traffic lanes, signs and lights. This ability is important because very few vehicles are autonomously driven, and must integrate with conventionally operated vehicles. An enhanced infrastructure has yet to be available solely for autonomous vehicles to more easily navigate lanes and intersections non-visually. Recognizing these cues efficiently can be a complicated task as it not only involves constantly gathering visual information from the vehicle’s surroundings, but also requires accurate real time processing. Ambiguity of traffic control signals challenges even the most advanced computer decision making algorithms. The vehicle then must keep a predetermined position within its travel lane based on its interpretation of its surroundings.
Technical Paper

Reducing Complexity in Routing of Non-Standard Intersections, to Aid in Autonomous Vehicle Navigation

Autonomous vehicles must possess the capability to navigate complex intersections, which do not conform to typical models. Such intersections may have multiple roadways of different classes, highly acute angles, or unique multi-modal combinations. These may include railway grade crossings, bicycle lanes, or unique signal arrangements. Conventional navigation systems, which gather data from the surrounding area then plan a path through the collected data require faultless and complex analysis of extremely unstructured environments. The vehicle must then avoid obstacles as well as successfully navigate the intersection with extremely low tolerance for error. Computer decision making challenges can arise from this method of navigation, especially when interacting with non-autonomous vehicles.
Technical Paper

Self-Driving Intelligent Vehicle to Increase Road Safety, Lower Congestion Rates and Decrease Emissions

This study presents the design and development of a vehicle platform with intelligent sensors that has the capabilities to drive independently and cooperatively on roads. An integrated active safety system has been designed to optimize the human senses using ultrasonic infrared sensors and transmitter/receiver modules, to increase the human vision, feel and communication for increased road safety, lower congestion rates, and decrease CO2 emissions. Ultrasonic sensors mounted on the platform, emitted longitudinal 40 kHz waves and received echoes of these sound waves when an object was within its direction. The duration was converted to a distance measurement to detect obstacles as well as using distance measurement threshold values to implement adaptive cruise control. Infrared sensors equipped with an IR LED and a bipolar transistor detected a change in light intensity to identify road lanes.