# A path planning and path-following control framework for a general 2-trailer with a car-like tractor

arXiv: https://arxiv.org/abs/1904.01651 · HTML: https://mlsanity.com/paper/1904.01651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Hybrid Machine Learning for Articulation Angle Estimation of Truck-Semitrailer Combinations](https://mlsanity.com/paper/2607.16758/) (2026)

## Abstract (arXiv)

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.