Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control

TL;DR

This paper introduces a novel primitive-based planning and data-driven control approach enabling an autonomous RC car to perform consecutive inertia drifts through complex paths, validated in simulation and real-world tests.

Contribution

It presents a new integrated planning and control framework that handles aggressive inertia drifts on complex paths for autonomous RC cars.

Findings

Successfully performs consecutive inertia drifts on complex paths

Validated approach in both simulation and real-world environments

Improves control robustness for aggressive maneuvers

Abstract

Inertia drift is an aggressive transitional driving maneuver, which is challenging due to the high nonlinearity of the system and the stringent requirement on control and planning performance. This paper presents a solution for the consecutive inertia drift of an autonomous RC car based on primitive-based planning and data-driven control. The planner generates complex paths via the concatenation of path segments called primitives, and the controller eases the burden on feedback by interpolating between multiple real trajectories with different initial conditions into one near-feasible reference trajectory. The proposed strategy is capable of drifting through various paths containing consecutive turns, which is validated in both simulation and reality.