Driver controls : The driver controller algorithm understands the position of the cones in the Lidar Point cloud. This algorithm has two objectives:.This is done by calculating the distance between consecutive points in the point cloud. The points that belong to the same cone are close to each other, while cones are relatively far apart from each other. After clustering, the midpoint of the cone is the mean of the position of all the points in the cone.įig 6. Plot showing cones detected in the point cloud Cone detection (Figure 6) : The goal of the cone detection algorithm is to cluster all points that belong to one cone and identify the position of the cones.The block returns a point cloud with the specified field of view and angular resolution. The environment is rendered using the Unreal Engine from Epic Games. The Simulation 3D Lidar block provides an interface to the lidar sensor in a 3D simulation environment. Lidar mounting : The purpose of the lidar is to measure the distance to the cones.
As mentioned in the previous section, the driverless vehicle is in an unknown environment that consists of cones kept on both sides of the track. To detect the cones and generate a reference path for the first lap, we have built a Simulink model as shown in Figure 3. Figure 4 shows the steps performed by the model in the first lap:įig 4. Block diagram representation of environment mapping Steps for creating a custom scene Lap1: Environment Mappingįig 3.
Alternatively, we can also use RoadRunner to design 3D scenes for automated driving simulations.įig 2.
#MATLAB DRIVE HOW TO#
To learn how to customize scenes (Figure 2), please follow the steps explained in the documentation. These 3D scenes are visualized using the Unreal Engine from Epic Games.Īs the current problem requires a customized scene, we used the Unreal Editor and the Vehicle Dynamics Blockset Interface for Unreal Engine 4 Projects support package to build the scene. The first step is to create a 3D simulation environment consisting of a vehicle, track, and cones. The Vehicle Dynamics Blockset Toolbox comes installed with prebuilt 3D scenes to simulate and visualize the vehicles modeled in Simulink. In this article, we will demonstrate an approach to drive an autonomous vehicle in a closed-loop circuit. The task here is to drive the car in an unknown environment avoiding collision with the cones ensuring to complete the necessary laps. Using MATLAB and Simulink, you can design automated driving system functionality including sensing, path planning, sensor fusion, and control systems. With advancements in the automotive industry, various student competitions have introduced the driverless category, where the goal of the teams is to design and build an autonomous vehicle that can compete in different disciplines. Today’s blog post is written by Veer Alakshendra, Education Technical Evangelist on the Student Competition team at MathWorks.