A FRAMEWORK FOR MULTI-SENSOR POSITIONING AND MAPPING FOR AUTONOMOUS VEHICLES

Abstract. Autonomous vehicles (AVs) are cars, buses or trucks that can travel with limited or no human intervention. They combine sensors, perception systems, and software to control, navigate, and safely drive the vehicle. AVs promise to improve the efficiency of our transportation system, reduce collisions and traffic congestion and improve mobility for our growing population. As the world continues to move toward a more efficient transportation system driven by AVs, there is a growing demand for new technologies that guarantee efficient and safe operation. Trust in AVs hinges on the reliability of autonomy, including the crucial task of positioning, which should be accurately provided at high precision everywhere for all environments. This paper introduces a framework for developing and deploying robust positioning and mapping systems for AVs at a submeter level of accuracy with high integrity. We introduce a new paradigm for the positioning and mapping of AVs that expands the capabilities of the present technologies and enables the processing of a broader range of complementary sensors and systems on a single platform. The target is to sustain this performance seamlessly everywhere in all operating and weather conditions relying on the suite of wireless and perception systems in addition to the vehicle's onboard sensors. Some sample results demonstrating the submeter level of positioning in various environments are discussed in this paper. This research will significantly impact the acceptance of and trust in AVs by enhancing safety and reliability and decreasing the failure rate in degraded environments.