Autonomous vehicles provide an opportunity to reduce highway congestion and emissions, while increasing highway safety. Intelligently routed vehicles will also be better integrated with existing traffic patterns, minimizing travel times. By reducing the time wasted in traffic; harmful emissions will consummately be reduced. Well-designed autonomous control systems provide for increased highway safety by reducing the frequency and severity of traffic accidents caused by driver error. In order to achieve this, a robust multi-layered control system must be designed, which minimizes the likelihood of computer error, while enabling seamless transition to and from human control.
Autonomous vehicle navigation systems rely on accurate and timely sensor inputs to determine a vehicle’s location, attitude, speed, and acceleration. This paper describes a telemetry sensor fusion approach, which enables an autonomous vehicle to navigate, complex intersections, 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 complement of environmental sensors, this path planning - path following approach enhances the robustness of autonomous vehicle operating models.
This research supports the rapidly expanding field of autonomous automobiles by examining novel concepts for robust telemetry sensor fusion between inertial, GPS, and wheel speed sensors, which allows for error correction and enhanced positional accuracy, when compared to conventional navigation algorithms.