Accurate automotive navigation systems are foundational to the efficacy and safety of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) technologies, necessitating continuous advancements to ensure reliability in the face of Global Navigation Satellite System (GNSS) outages and dynamic road conditions. Innovative techniques and algorithms have been developed and critically evaluated to enhance various facets of navigation performance, marking significant strides in vehicular navigation research.
The exploration begins with advancements in odometry-based navigation independent of map data. Integration of Structure-from-Motion (SfM) with the reduced inertial sensor system (RISS) and GNSS substantially improves performance, particularly in managing complex road maneuvers. To better handle urban environments, a novel Semantic Segmentation-based Outlier Rejection (SS-OR) technique was developed to enhance the accuracy of visual odometry systems, presenting notable implications for autonomous navigation and mapping.
Further investigation into automotive Wheel Speed Sensors (WSS) leads to the creation of a fusion engine that amalgamates WSS data with stereo visual odometry. This approach effectively minimizes forward velocity errors and biases, contributing significantly to the overall enhancement of navigation system capabilities.
The discourse extends to map-aided navigation, introducing a Two-Stage Kinematic Update technique for topological map-matching (TMM) algorithms reliant on conventional GNSS/RISS integration. This innovation demonstrates considerable improvements in navigation accuracy during GNSS outages. Nonetheless, the persistence of challenges related to cumulative errors and drift underscores the necessity for expanded integration with additional perception systems.
Concluding the research, the integration of topological maps with the proposed forward velocity fusion engine and the dynamic window approach (DWA) culminates in a comprehensive navigation solution. Although the outcomes are promising, opportunities for further enhancements remain, particularly in refining map details and addressing deviations from planned routes, paving the way for future explorations in the domain.
The efficacy of the proposed methods is rigorously evaluated through a series of real-road experiments conducted in the Cities of Kingston and Toronto, designed to assess their viability and advantages comprehensively. The outcomes of these tests reveal a marked improvement in performance, showcasing the proposed methods' superiority in comparison to traditional navigation techniques.