java 车辆轨迹补全代码

车辆轨迹补全，通常需要依赖于车载传感器和地图数据，结合算法对车辆轨迹进行预测和补全。以下是一个简单的 Java 代码示例，用于对车辆轨迹进行补全：

public class TrajectoryCompletion {

    // 车辆轨迹补全算法，输入为车辆当前位置和历史轨迹数据
    public List<Point> completeTrajectory(Point currentPos, List<Point> historyTrajectory) {
        List<Point> result = new ArrayList<>();
        if (historyTrajectory.size() < 2) {
            // 历史轨迹不足，无法进行补全
            return result;
        }
        // 计算历史轨迹的平均速度
        double avgSpeed = calculateAvgSpeed(historyTrajectory);
        // 计算当前位置到历史轨迹最后一个点的距离
        double distance = calculateDistance(currentPos, historyTrajectory.get(historyTrajectory.size() - 1));
        // 计算当前位置到历史轨迹最后一个点的时间
        double time = distance / avgSpeed;
        // 根据历史轨迹的末尾点和预测时间，预测车辆的轨迹
        Point predictedPos = predictPosition(historyTrajectory.get(historyTrajectory.size() - 1), time);
        // 将预测点加入轨迹列表
        result.addAll(historyTrajectory);
        result.add(predictedPos);
        return result;
    }

    // 计算轨迹平均速度
    private double calculateAvgSpeed(List<Point> trajectory) {
        double totalDistance = 0;
        double totalTime = 0;
        for (int i = 1; i < trajectory.size(); i++) {
            Point p1 = trajectory.get(i - 1);
            Point p2 = trajectory.get(i);
            totalDistance += calculateDistance(p1, p2);
            totalTime += (p2.getTime() - p1.getTime());
        }
        return totalDistance / totalTime;
    }

    // 计算两点之间距离
    private double calculateDistance(Point p1, Point p2) {
        double dx = p1.getX() - p2.getX();
        double dy = p1.getY() - p2.getY();
        return Math.sqrt(dx * dx + dy * dy);
    }

    // 预测车辆位置
    private Point predictPosition(Point lastPos, double time) {
        double x = lastPos.getX() + lastPos.getSpeed() * Math.cos(lastPos.getHeading()) * time;
        double y = lastPos.getY() + lastPos.getSpeed() * Math.sin(lastPos.getHeading()) * time;
        double heading = lastPos.getHeading();
        double speed = lastPos.getSpeed();
        long timestamp = lastPos.getTime() + (long) (time * 1000);
        return new Point(x, y, heading, speed, timestamp);
    }
}

class Point {
    private double x;
    private double y;
    private double heading;
    private double speed;
    private long time;

    public Point(double x, double y, double heading, double speed, long time) {
        this.x = x;
        this.y = y;
        this.heading = heading;
        this.speed = speed;
        this.time = time;
    }

    public double getX() {
        return x;
    }

    public double getY() {
        return y;
    }

    public double getHeading() {
        return heading;
    }

    public double getSpeed() {
        return speed;
    }

    public long getTime() {
        return time;
    }
}

该代码实现了一个简单的车辆轨迹补全算法，输入为车辆当前位置和历史轨迹数据，输出为预测后的轨迹。该算法基于历史轨迹的平均速度和当前位置到历史轨迹最后一个点的距离，预测车辆在未来一段时间内的位置，并将预测点加入轨迹列表中。