ChatGPT Java：如何实现自动驾驶与交通规划

import org.opencv.core.Mat;
import org.opencv.videoio.VideoCapture;

public class SensorData {
    private VideoCapture camera;

    public SensorData() {
        // 初始化摄像头
        camera = new VideoCapture(0);
    }

    public Mat getCameraImage() {
        Mat image = new Mat();
        // 读取摄像头图像
        camera.read(image);
        return image;
    }

    public void close() {
        // 释放摄像头资源
        camera.release();
    }
}

import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.dnn.Dnn;
import org.opencv.dnn.Net;

public class ObjectDetection {
    private Net net;

    public ObjectDetection() {
        // 加载预训练好的模型
        String modelPath = "path_to_model";
        String configPath = "path_to_config";
        net = Dnn.readNetFromDarknet(configPath, modelPath);
    }

    public void detectObjects(Mat image) {
        // 对图像进行预处理
        Mat blob = Dnn.blobFromImage(image, 1.0, new Size(416, 416), new Scalar(0, 0, 0), true, false);
        net.setInput(blob);

        // 运行模型进行目标检测
        Mat output = net.forward();

        // 处理输出结果
        MatOfRect detections = new MatOfRect(output);
        Rect[] rects = detections.toArray();
        // 在图像上绘制检测框
        for (Rect rect : rects) {
            Imgproc.rectangle(image, rect.tl(), rect.br(), new Scalar(0, 255, 0), 2);
        }
    }
}

import org.jgrapht.Graph;
import org.jgrapht.GraphPath;
import org.jgrapht.alg.shortestpath.DijkstraShortestPath;
import org.jgrapht.graph.DefaultWeightedEdge;
import org.jgrapht.graph.SimpleDirectedWeightedGraph;

public class PathPlanning {
    private Graph<String, DefaultWeightedEdge> graph;
    private DijkstraShortestPath<String, DefaultWeightedEdge> dijkstra;

    public PathPlanning() {
        // 创建带权重的有向图
        graph = new SimpleDirectedWeightedGraph<>(DefaultWeightedEdge.class);
        graph.addVertex("A");
        graph.addVertex("B");
        graph.addVertex("C");
        graph.addVertex("D");
        DefaultWeightedEdge AB = graph.addEdge("A", "B");
        graph.setEdgeWeight(AB, 1);
        DefaultWeightedEdge BC = graph.addEdge("B", "C");
        graph.setEdgeWeight(BC, 2);
        DefaultWeightedEdge CD = graph.addEdge("C", "D");
        graph.setEdgeWeight(CD, 3);

        // 创建最短路径算法对象
        dijkstra = new DijkstraShortestPath<>(graph);
    }

    public GraphPath<String, DefaultWeightedEdge> findShortestPath(String source, String target) {
        // 计算最短路径
        return dijkstra.getPath(source, target);
    }
}

import java.util.Random;

public class ObstacleAvoidance {
    private double obstacleDistance;

    public ObstacleAvoidance() {
        // 模拟距离传感器距离
        Random random = new Random();
        obstacleDistance = random.nextDouble();
    }

    public boolean isObstacleDetected() {
        // 判断是否检测到障碍物
        return obstacleDistance < 0.5;
    }

    public void stop() {
        // 停止汽车
        System.out.println("Car stopped.");
    }
}