Darwin Kuan

IROS Conference 1988 Conference Paper

Obstacle Detection And lntelligent Navigation Using Range Data

• Uma Kant Sharma
• Darwin Kuan

ICRA Conference 1987 Conference Paper

Model based geometric reasoning for autonomous road following

• Darwin Kuan
• Uma Kant Sharma

This paper describes a model-based geometric reasoning module for autonomous road following. Vision-guided road following requires extracting road boundaries from images in real-time to guide the navigation of autonomous vehicles on roadway. The detected road region boundary is error-prone due to imperfect image segmentation. To achieve robust system performance, a geometric reasoning module that uses spatial and temporal constraints to perform model-based reasoning is required. Local geometric supports for each road edge segment are collected and recorded and global consistency checking is performed to obtain a consistent interpretation of the raw data. Cases involving incomplete sensor data, on curved roads where only one side of the road is visible, and incorrect segmentation due to shadows, road patches, or unusual road conditions, can usually be detected and corrected. This reasoning module has been integrated into a road following system which is capable of supporting autonomous road following at 19 km/hr.

AAAI Conference 1986 Conference Paper

A Real-Time Road Following and Road Junction Detection Vision System for Autonomous Vehicles

• Darwin Kuan

This paper describes a real-time road following and road junction detection vision system for autonomous vehicles. Vision-guided road following requires extracting road boundaries from images in real-time to guide the navigation of autonomous vehicles on the roadway. We use a histogram-based pixel classification algorithm to classify road and non-road regions in the image. The most likely road region is selected and a polygonal representation of the detected road region boundary is used as the input to a geometric reasoning module that performs model-based reasoning to accurately identify consistent road segments and road junctions. In this module, local geometric supports for each road edge segment are collected and recorded and a global consistency checking is performed to obtain a consistent interpretation of the raw data. Limited cases of incorrect image segmentation due to shadows or unusual road conditions can be detected and corrected based on the road model. Similarly, road junctions can be detected using the same principle. The real-time road following vision system has been implemented on a high-speed image processor connected to a host computer. We have tested our road following vision system and vehicle control system on a gravel road. The vehicle can travel up to 8 kilometers per hour speed on the road.