Obstacle avoidance of autonomous vehicles based on model predictive control

Abstract

This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. Safe trajectories are generated using the non-linear model predictive framework, in which the simplified dynamics of the vehicle are used to predict the state of the vehicle over the look-ahead horizon. To compensate for the slight dissimilarity between the simplified model and the actual vehicle, a separate controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle are controlled using the inverse dynamics of the vehicle powertrain model, and the lateral dynamics are controlled using a linear quadratic regulator. In the non-linear model predictive framework, to obtain safe trajectories, local obstacle information is incorporated into the performance index using a parallax-based method. Simulation results on a full non-linear vehicle model show that the proposed combination of model-predictive-control-based trajectory generation and tracking controller gives satisfactory online obstacle avoidance performance.

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

“…Examples of off-line path planning systems, which can find near optimal trajectories for a set of vehicles are available, see, e.g., [151,256]. Another variation of this problem involves a precomputed prescription of the paths to be followed, where the navigation law must only find an appropriate velocity profile that avoids collisions (see, e.g., [56,209]). …”

Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model

“…For example, [4] compares a motion planner and control MPC using a 2 Degrees of Freedom (2 DoF) linear-tire dynamic bicycle model with a 14 DoF vehicle model. The validity of this dynamic bicycle model is guaranteed by constraining the lateral acceleration to 0.5g on normal road conditions, condition which has been derived in [5]. However, the practical implementation of this system on a real vehicle is limited as it assumes a constant velocity.…”

Determining the maximum time horizon for vehicles to safely follow a trajectory

“…In [19], MPC is utilized for lane departure prevention. An algorithm which generates collisionfree trajectories in a static environment is proposed in [20]. In [21], collision avoidance is achieved through steering and braking, under the assumption that the obstacles move with constant velocity.…”