Designing a local planner to control a tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem for autonomous driving systems. This thesis considers the critical situation of the stability of tractor-trailer systems. A practical and novel approach is presented to design an non-linear Model Predictive Control (NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor’s velocity and steering angle are considered as control variables;​ the proposed NMPC local planner is designed to handle jackknife situations, to avoid multiple static obstacles, and to path follow a certain path in both forward and backward maneuvering. The above-mentioned challenges are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem into an non-linear programming problem (NLP) that can be solved by the Interior Point Optimizer (IPOPT) in CasADi optimization toolbox. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time to achieve asymptotic stability in avoiding static obstacles and in accurate tracking performance while respecting path constraints. Finally, the NMPC local planner is integrated with an open-source autonomous driving software stack called Autoware.ai.