HelperMoveEgoVehicleToState(egoVehicle, currentEgoState) Įlse % All trajectories either violated kinematic feasibility % constraints or resulted in a collision. % Move ego with optimal trajectory if ~isempty(optimalTrajectory)ĬurrentEgoState = optimalTrajectory.Trajectory(2,:) IsKinematicsFeasible, isCollisionFree, idx) ĭisplay(scenario, egoVehicle, lidars, ptClouds, tracker, tracks, trajectories, collisionValidator) Trajectories = helperAssembleTrajectoryForPlotting(globalTrajectories. OptimalTrajectory = nonCollidingGlobalTrajectories(idx) NonCollodingCollisionProb = collisionProb(isCollisionFree) Ĭosts = helperCalculateTrajector圜osts(nonCollidingFrenetTrajectories, nonCollodingCollisionProb, speedLimit) NonCollidingFrenetTrajectories = feasibleFrenetTrajectories(isCollisionFree) NonCollidingGlobalTrajectories = feasibleGlobalTrajectories(isCollisionFree) % Calculate costs and final optimal trajectory = isTrajectoryValid(collisionValidator, feasibleGlobalTrajectories) % Calculate collision validity of feasible trajectoriesįeasibleGlobalTrajectories = globalTrajectories(isKinematicsFeasible) įeasibleFrenetTrajectories = frenetTrajectories(isKinematicsFeasible) IsKinematicsFeasible = helperKinematicFeasibility(frenetTrajectories,speedLimit,accMax) % Calculate kinematic feasibility of generated trajectories = helperGenerateTrajectory(connector, refPath, currentEgoState, speedLimit, laneWidth, intersectionS, intersectionBuffer) % Sample trajectories using current ego state and some kinematic % parameters Step(collisionValidator, currentEgoState, map, time)
#OCCUPANCY GRID MAPPING MATLAB CODE UPDATE#
% Update validator's future predictions using current estimate = tracker(sensorData,sensorConfigs,time) SensorData = packAsSensorData(ptClouds,sensorConfigs,time) SensorConfigs,egoVehicle) Įnd % Pack point clouds as sensor data format required by the tracker % Fill in sensor configurations for i = 1:numel(sensorConfigs) % Set up sensor configurations for each lidar The definition of scenario and sensors is wrapped in the helper function helperGridBasedPlanningScenario. For more details on the scenario and sensor models, refer to the Grid-Based Tracking in Urban Environments Using Multiple Lidars (Sensor Fusion and Tracking Toolbox) example. The ego vehicle is equipped with six homogenous lidar sensors, each with a field of view of 90 degrees, providing 360-degree coverage around the ego vehicle. The scenario used in this example represents an urban intersection scene and contains a variety of objects, including pedestrians, bicyclists, cars, and trucks. In this example, you obtain the grid-based estimate of the environment by fusing point clouds from six lidars mounted on the ego vehicle. Further, the estimates from the dynamic grid can be predicted for a short-time in the future to assess the occupancy of the local environment in the near future. In addition to estimating the probability of occupancy, the dynamic occupancy grid also estimates the kinematic attributes of each cell, such as velocity, turn-rate, and acceleration. A dynamic occupancy grid map is a grid-based estimate of the local environment around the ego vehicle. For an example using the discrete set of objects, refer to the Highway Trajectory Planning Using Frenet Reference Path example. In this example, you represent the surrounding environment as a dynamic occupancy grid map. A hybrid of these two approaches is also possible by extracting object hypothesis from the grid-based representation. The grid-based representation is also less sensitive to imperfections of object extraction such as false and missed targets. On the other hand, a grid-based approach allows for an object-model-free representation, which assists in efficient collision-checking in complex scenarios with large number of objects. It also allows for an easier way to define inter-object relations for behavior prediction. For planning algorithms, the object-based representation offers a memory-efficient description of the environment. The choice of environment representation is typically governed by the upstream perception algorithm. In the presence of dynamic obstacles in the environment, a local motion planner requires short-term predictions of the information about the surroundings to assess the validity of the planned trajectories.
#OCCUPANCY GRID MAPPING MATLAB CODE FREE#
Discrete set of objects in the surrounding environment with defined geometries.ĭiscretized grid with estimate about free and occupied regions in the surrounding environment.
0 kommentar(er)
