Intersection-Traffic Control of Autonomous Vehicles using Newton-Raphson Flows and Barrier Functions
This work addresses safety and efficiency in autonomous vehicle navigation at intersections, representing an incremental improvement by integrating existing methods.
The paper tackles trajectory control for autonomous vehicles at intersections by applying a nonlinear tracking technique based on Newton-Raphson flows, which separates target trajectory computation from control, and ensures safety using control barrier functions.
This paper concerns an application of a recently-developed nonlinear tracking technique to trajectory control of autonomous vehicles at traffic intersections. The technique uses a flow version of the Newton-Raphson method for controlling a predicted system-output to a future reference target. Its implementations are based on numerical solutions of ordinary differential equations, and it does not specify any particular method for computing its future reference trajectories. Consequently it can use relatively simple algorithms on crude models for computing the target trajectories, and more-accurate models and algorithms for trajectory control in the tight loop. We demonstrate this point at an extant predictive traffic planning-and-control method with our tracking technique. Furthermore, we guarantee safety specifications by applying to the tracking technique the framework of control barrier functions.