Introduction

Automatically parking a car that is left in front of a parking lot is a challenging problem. The vehicle's automated systems are expected to take over and steer the vehicle to an available parking spot. This example focuses on planning a feasible path through the environment, generating a trajectory from this path, and using a feasible controller to execute the trajectory. Map creation and dynamic obstacle avoidance are excluded from this example.

Before simulation, thehelperSLCreateCostmapfunction is called within thePreLoadFcncallback function of the model. For details on using callback functions, seeModel Callbacks(Simulink). ThehelperSLCreateCostmapfunction creates a static map of the parking lot that contains information about stationary obstacles, road markings, and parked cars. The map is represented as avehicleCostmapobject.

To use thevehicleCostmapobject in Simulink®, thehelperSLCreateUtilityStructfunction converts thevehicleCostmapinto a struct array in the block's mask initialization. For more details, seeInitialize Mask(Simulink).

The global route plan is described as a sequence of lane segments to traverse to reach a parking spot. Before simulation, thePreLoadFcncallback function of the model loads a route plan, which is stored as a table. The table specifies the start and end poses of the segment, as well as properties of the segment, such as the speed limit.

routePlan = 5×3 table StartPose EndPose Attributes ________________ ____________________ __________ 4 12 0 56 11 0 1×1 struct 56 11 0 70 19 90 1×1 struct 70 19 90 70 32 90 1×1 struct 70 32 90 52 38 180 1×1 struct 53 38 180 36.3 44 90 1×1 struct

The inputs and outputs of many blocks in this example are Simulink buses (Simulink.Bus(Simulink)classes). In thePreLoadFcncallback function of the model, thehelperSLCreateUtilityBusfunction creates these buses.

Planning is a hierarchical process, with each successive layer responsible for a more fine-grained task. The behavior layer [1] sits at the top of this stack. TheBehavior Plannerblock triggers a sequence of navigation tasks based on the global route plan by providing an intermediate goal and configuration for theMotion PlanningandTrajectory Generationblocks. Each path segment is navigated using these steps:

Explore the Subsystems

The Vehicle Controller subsystem contains aLateral Controller Stanleyblock and aLongitudinal Controller Stanleyblock to regulate the pose and the velocity of the vehicle, respectively. To handle realistic vehicle dynamics [3], theVehicle modelparameter in the Lateral Controller Stanley block is set toDynamic bicycle model. With this configuration, additional inputs, such as the path curvature, the current yaw rate of the vehicle, and the current steering angle are required to compute the steering command. The Longitudinal Controller Stanley block uses a switching Proportional-Integral controller to calculate the acceleration and the deceleration commands that actuate the brake and throttle in the vehicle.

To demonstrate the performance, the vehicle controller is applied to the Vehicle Model block, which contains a simplified steering system [3] that is modeled as a first-order system and aVehicle Body 3DOF(Vehicle Dynamics Blockset)block shared between Automated Driving Toolbox™ and Vehicle Dynamics Blockset™. Compared with the kinematic bicycle model used in theAutomated Parking ValetMATLAB® example, this Vehicle Model block is more accurate because it considers the inertial effects, such as tire slip and steering servo actuation.

Simulation Results

The Visualization block shows how the vehicle tracks the reference path. It also displays vehicle speed and steering command in a scope. The following images are the simulation results for this example:

Simulation stops at about 45 seconds, which is when the vehicle reaches the destination.

Conclusions

This example shows how to implement an automated parking valet in Simulink.

References

[1]部ehler, Martin, Karl Iagnemma, and Sanjiv Singh.The DARPA Urban Challenge: Autonomous Vehicles in City Traffic(1st ed.). Springer Publishing Company, Incorporated, 2009.

[2] Lepetic, Marko, Gregor Klancar, Igor Skrjanc, Drago Matko, and Bostjan Potocnik, "Time Optimal Path Planning Considering Acceleration Limits."Robotics and Autonomous Systems, Volume 45, Issues 3-4, 2003, pp. 199-210.

[3] Hoffmann, Gabriel M., Claire J. Tomlin, Michael Montemerlo, and Sebastian Thrun. "Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing."American Control Conference, 2007, pp. 2296-2301.