A practical MPC method for autonomous driving longitudinal dynamic control's real-world challenges

Autonomous driving's Planning-and-Control (PnC) integration demands alignment in vehicle motion feasibility and motion error predictability, which requires the motion controller to respect realistic vehicle system constraints and dynamic properties. This paper describes a Model Predictive Control (MPC) method that practically handles the system challenges in vehicle longitudinal dynamic control, introduced by complex torque capacity shapes, system switching by gear shifts, and multiple actuation systems. Techniques of constraint local affine approximation, wheel and actuator domain separation, and fuel mapping blending are invented to address the aforementioned challenges, leading to quasi-optimal control solution using minimal computation time. Through formulating the control problem into constrained multi-objective optimizations, product & functional requirements involved in autonomous driving, such as tracking response, safety constraints, fuel economy, ride comfort, are conveniently handled and explicitly satisfied over a wide range of scenarios using a single control core solver. This controller has been sufficiently validated and supports TuSimple's class-8 truck autonomous driving operations in real traffic of Arizona and Texas in USA.