Abstract
Autonomous driving in dense urban areas presents an especially difficult task. First, globally localizing information, such as GPS signal, often proves to be unreliable in such areas due to signal shadowing and multipath errors. Second, the high‐definition environmental maps with sufficient information for autonomous navigation require a large amount of data to be collected from these areas, significant postprocessing of this data to generate the map, and then continual maintenance of the map to account for changes in the environment. This paper addresses the issue of autonomous driving in urban environments by investigating algorithms and an architecture to enable fully functional autonomous driving with little to no reliance on map‐based measurements or GPS signals. An extended Kalman filter with odometry, compass, and sparse landmark measurements as inputs is used to provide localization. Real‐time detection and estimation of key roadway features are used to create an understanding of the surrounding static scene. Navigation is accomplished by a compass‐based navigation control law. Experimental scene understanding results are obtained using computer vision and estimation techniques and demonstrate the ability to probabilistically infer key features of an intersection in real time. Key results from Monte Carlo studies demonstrate the proposed localization and navigation methods. These tests provide success rates of urban navigation under different environmental conditions, such as landmark density, and show that the vehicle can navigate to a goal nearly 10 km away without any external pose update at all. Field tests validate these simulated results and demonstrate that, for given test conditions, an expected range can be determined for a given success rate.
Highlights
In the past decade, the autonomous driving industry has experienced rapid growth and development of key technologies
The Linear Hydraulic Actuator Characterization Device (LHACD) allowed for characterization tests of the hydraulic artificial muscles to be performed by measuring the quasistatic force-stroke capabilities
Due to the versatility and convenience that the LHACD offers for actuator testing and controller development, these and related tests can be extended to a wide range of hydraulic actuators for a myriad of applications in robotics, industrial automation, or aerospace
Summary
The autonomous driving industry has experienced rapid growth and development of key technologies. State-of-the-art positioning systems struggle to provide the level of localization needed for autonomous driving due to the difficulties that dense urban environments present [34]. Security of autonomous vehicles is a major concern, as both GPS measurements and maps can be spoofed and/or jammed [35] Given these challenges, this study investigates alternative architectures and sources of information that may be used for robust navigation of urban roadways. An alternative method of obtaining an HD map of the environment involves the use of unmanned aerial vehicles, equipped with lidar and camera systems, as a novel platform for photogrammetry [58] This method requires timeconsuming data collection with heavy data post-processing. Due to the extreme complexity and laborious nature of the HD mapping task, automakers such as Volkswagen, BMW, and General Motors have relied on third-party services, such as HERE and MobilEye, to provide these HD maps [59]
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