车辆行驶环境感知是自动驾驶领域的重点和难点问题,其中车道线检测是车辆行驶环境感知的基础。针对不同实例车道线难以区分、现有区分算法时间复杂度高、不同行驶场景需人为调整超参数等问题,提出了一种三分支车道线实例分割算法,并对分割结果进行自适应聚类以拟合不同实例车道线。针对车载摄像头获取的图像数据不均衡特点,用基于三分视野法的Tversky Loss函数对卷积神经网络进行训练。针对车道线大曲率半径的特点,以高次项权重作为最小二乘法正则项拟合车道线。在TuSimple数据集上的测试结果表明,本算法对实例车道线的识别准确率为96.23%,相比LaneNet,本算法的时间消耗减少了23.67%,且对不同车辆行驶场景都有较好的检测效果。

Vehicle driving environment perception is a key and difficult problem of automatic driving field, among which lane detection is the foundation of vehicle driving environment perception. In view of the difficulty in distinguishing different lane instances, the high time complexity of existing distinguishing algorithms, and the need to manually adjust hyperparameters in different driving scenes, a three-branch lane instance segmentation algorithm is proposed in this paper, and the segmentation results are adaptively clustered to fit lanes of different instances. Considering the unbalanced characteristic of the image data obtained by the vehicle-mounted camera, a convolutional neural network is trained on the basis of the Tversky Loss function of the three-section field of view method. In view of the large curvature radius of the lane, the weight of the higher-order term is used as the regular term of a least square method to fit lanes. The test results on the TuSimple dataset show that the accuracy of the algorithm in identifying the lane of the considered example is 96.23%. Compared with LaneNet, the time complexity of the algorithm is reduced by 23.67%. Additionally, it has a good detection effect for various vehicle driving scenes.