Presentation Title

Telemetry Based Location Sensor Fusion and Error Correction in Intelligent Vehicles

Location

Room 2911

Session Format

Paper Presentation

Research Area Topic:

Engineering and Material Sciences - Mechanical

Abstract

Intelligent vehicle navigation systems rely on accurate and timely sensor input to determine a vehicle‰Ûªs location, attitude, speed, and acceleration. External sensors provide the primary guidance data required to determine a vehicle's current and planned location, as well as path planning data, relative to the environment around the vehicle. This paper describes a telemetry sensor fusion approach, which enables an intelligent vehicle to navigate, over short distances, based on previously planned paths and near field sensors. This reduces computational overhead on the vehicle's computer, and provides real time redundancy for system errors or delays. In conjunction with a full compliment of environmental sensors, this path planning - path following approach enhances the robustness of intelligent vehicle operating models. Simulation validated by small-scale tests indicates that telemetry sensor fusion provides a viable method for short distance navigation, based on pre-planned paths. This research supports the rapidly expanding field of intelligent automobiles by examining novel concepts for robust telemetry sensor fusion, which allows for error correction and enhanced positional accuracy, when compared to conventional navigation algorithms. This concept will improve the safety and performance of autonomous vehicles by providing path following methods which enable obstacle avoidance and navigation through complex environments. Research methods include simulation, small-scale modeling, and meso-scale application testing. Sensor fusion algorithms were created which enable the fusion of multiple on board telemetry sensors to determine the vehicle localization and trajectory, with sufficient accuracy to navigate complex planned paths. Small scale tests validated the sensor fusion algorithms developed, and ongoing meso-scale testing enables human machine interaction on.

Presentation Type and Release Option

Presentation (Open Access)

Start Date

4-16-2016 4:00 PM

End Date

4-16-2016 5:00 PM

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Apr 16th, 4:00 PM Apr 16th, 5:00 PM

Telemetry Based Location Sensor Fusion and Error Correction in Intelligent Vehicles

Room 2911

Intelligent vehicle navigation systems rely on accurate and timely sensor input to determine a vehicle‰Ûªs location, attitude, speed, and acceleration. External sensors provide the primary guidance data required to determine a vehicle's current and planned location, as well as path planning data, relative to the environment around the vehicle. This paper describes a telemetry sensor fusion approach, which enables an intelligent vehicle to navigate, over short distances, based on previously planned paths and near field sensors. This reduces computational overhead on the vehicle's computer, and provides real time redundancy for system errors or delays. In conjunction with a full compliment of environmental sensors, this path planning - path following approach enhances the robustness of intelligent vehicle operating models. Simulation validated by small-scale tests indicates that telemetry sensor fusion provides a viable method for short distance navigation, based on pre-planned paths. This research supports the rapidly expanding field of intelligent automobiles by examining novel concepts for robust telemetry sensor fusion, which allows for error correction and enhanced positional accuracy, when compared to conventional navigation algorithms. This concept will improve the safety and performance of autonomous vehicles by providing path following methods which enable obstacle avoidance and navigation through complex environments. Research methods include simulation, small-scale modeling, and meso-scale application testing. Sensor fusion algorithms were created which enable the fusion of multiple on board telemetry sensors to determine the vehicle localization and trajectory, with sufficient accuracy to navigate complex planned paths. Small scale tests validated the sensor fusion algorithms developed, and ongoing meso-scale testing enables human machine interaction on.