To solve the problem of inaccurate object segmentation caused by unbalanced samples for in-vehicle point cloud, an improved semantic segmentation network RangeNet++ based on asymmetric loss function (AsL-RangeNet++) is proposed, which uses asymmetric loss (AsL) function and Adam optimizer to calculate and adjust object weights, achieve optimal point cloud segmentation. AsL-RangeNet++ can solve the problem of unbalance between positive and negative samples and label error in multi-label classification by calculating the weights of positive and negative samples respectively and more accurately segments the point cloud of small targets. A large number of experiments on the widely used SemanticKITTI dataset show that the proposed method has higher segmentation accuracy and better adaptability than the current mainstream methods.INDEX TERMS Semantic segmentation, rangenet++, asymmetric loss function, in-vehicle point cloud.