Context and Orientation Aware Path Tracking

Autonomous vehicles on city roads and especially in pedestrian environments require agility to navigate narrow passages and turn in tight spaces, leading to the need for a real-time, robust and adaptable controller. In this paper, we present orientation and context aware controllers for autonomous vehicles that can closely track the reference path wit alh respect to the current state of the vehicle, environmental properties, and the desired target orientation at the desired target location. Our proposed controllers are derived from the widely used pure pursuit controller. We validate our proposed controllers with respect to the baseline pure pursuit controller in simulation and on a full-size autonomous vehicle in a pedestrian environment. Our experimental results suggest significant improvements in adaptability and tracking performance compared to the pure pursuit controller.

Authors

• Nicholas Michael Bünger
• Sahil Panjwani
• Malika Meghjani
• Zefan Huang
• Marcelo H. Ang
• Daniela Rus

Keywords

• Space vehicles
• Context-aware services
• Target tracking
• Navigation
• Roads
• Urban areas
• Aerospace electronics
• Path Tracking
• Autonomous Vehicles
• Context-aware
• Vehicle State
• City Streets
• Target Orientation
• Reference Path
• Tight Spaces
• Optimal Control
• Tuning Parameter
• Current Position
• Dynamic Control
• Inertial Measurement Unit
• Middle Point
• Shape Of Profile
• High Curvature
• Yaw Angle
• Model-based Control
• Steering Angle
• Sharp Turn
• Geometric Control
• Current Head
• Path Curvature
• Maximum Deceleration
• Vehicle Simulation
• Steering Input