The Road 'More' Traveled: A Foundation for Autonomous Roadway Operations

摘要

Under the DARPA MARS 2020 program, Perceptek has developed a technical foundation for performing roadway operations in both structured and unstructured environments. Fully autonomous roadway operations require a large set of atomic functionalities that must seamlessly perform in concert in complex and dynamic environments. PercepTek has developed multiple atomic functionalities and implemented a robot control architecture called ARTEA (Autonomous Robotic Test and Evaluation Architecture) that blends these atomic functionalities into one cohesive system that can perform complex missions and reason about its environment. Some of the atomic functionalities that have been implemented and integrated onto our robotic test platform are vision-based road-following for both structured and unstructured roads, vision/radar-based vehicle-following, safety gap maintenance, road feature detection and response, road sign detection/recognition, and pedestrian detection. In this paper, technical details of each of the individual robotic functionalities are presented along with their performance and limitations. We then discuss some of the critical components of the ARTEA architecture that are used for blending inputs from disparate functionalities and performing reasoning about the environment. Field testing was a critical aspect of our development process and we will discuss the test platform that was used to develop and test our robotic system. At the culmination of our MARS 2020 effort we performed a robotic test drive from Denver Colorado to New Orleans Louisiana in which we tested and evaluated various aspects of our system. We finally discuss our performance and the limitations of our system for this drive.