[1] BAYU A, WIBISONO A, WISESA H A et al. Semantic segmentation of lidar point cloud in rural area[C], 73-78(1).

[2] MIHAI S, SHAH P, MAPP G et al. Towards autonomous driving： a machine learning-based Pedestrian Detection System Using 16-Layer LiDAR[C], 271-276(18).

[3] [3] 伍锡如， 薛其威. 基于激光雷达的无人驾驶系统三维车辆检测［J］. 光学 精密工程， 2022， 30（4）：489-497. doi: 10.37188/OPE.20223004.0489WUX R， XUEQ W. 3D vehicle detection for unmanned driving systerm based on lidar［J］. Opt. Precision Eng.， 2022， 30（4）：489-497.（in Chinese）. doi: 10.37188/OPE.20223004.0489

[4] RAGURAMAN S J, PARK J. Intelligent drivable area detection system using camera and lidar sensor for autonomous vehicle[C], 429-436(2020).

[5] DOVIS F, IMAM R, QIN W J et al. Opportunistic use of gnss signals to characterize the environment by means of machine learning based processing[C], 9190-9194(4).

[6] JOUBERT N, REID T G R, NOBLE F. Developments in modern gnss and its impact on autonomous vehicle architectures[C], 2029-2036(2020).

[7] NAVARRO M, ARRIBAS J, VILÀ-VALLS J et al. Hybrid GNSS/INS/UWB positioning for live demonstration assisted driving[C], 3294-3301(27).

[8] CHEN C, XIONG G M, ZHANG Z H et al. 3D LiDAR-GPS/IMU calibration based on hand-eye calibration model for unmanned vehicle[C], 337-341(27).

[9] [9] 王中宇， 倪显扬， 尚振东. 利用卷积神经网络的自动驾驶场景语义分割［J］. 光学 精密工程， 2019， 27（11）： 2429-2438. doi: 10.3788/ope.20192711.2429WANGZ Y， NIX Y， SHANGZ D. Autonomous driving semantic segmentation with convolution neural networks［J］. Opt. Precision Eng.， 2019， 27（11）： 2429-2438.（in Chinese）. doi: 10.3788/ope.20192711.2429

[10] LEE K F, CHEN X Z, YU C W et al. An intelligent driving assistance system based on lightweight deep learning models[J]. IEEE Access, 10, 111888-111900(2022).

[11] [11] 孙建波， 张叶， 常旭岭. 基于改进Mask R-CNN+LaneNet的车载图像车辆压线检测［J］. 光学 精密工程， 2022， 30（7）：854-868. doi: 10.37188/ope.20223007.0854SUNJ B， ZHANGY， CHANGX L. Vehicle pressure line detection based on improved Mask R-CNN+LaneNet［J］. Opt. Precision Eng.， 2022， 30（7）：854-868.（in Chinese）. doi: 10.37188/ope.20223007.0854

[12] HE K M, GKIOXARI G, DOLLÁR P et al. Mask R-CNN[C], 2980-2988(22).

[13] WANG Y Q, XU Z L, SHEN H et al. Centermask： single shot instance segmentation with point representation[C], 9310-9318(13).

[14] BOLYA D, ZHOU C, XIAO F Y et al. YOLACT： real-time instance segmentation[C], 9156-9165(2019).

[15] CHEN H, SUN K Y, TIAN Z et al. Blendmask： top-down meets bottom-up for instance segmentation[C], 8570-8578(13).

[16] YANG L J, FAN Y C, XU N. Video instance segmentation[C], 5187-5196.

[17] CAO J L, ANWER R M, CHOLAKKAL H et al[M]. SipMask： Spatial Information Preservation for Fast Image and Video Instance Segmentation, 1-18(2020).

[18] LI M H, LI S, LI L D et al. Spatial feature calibration and temporal fusion for effective one-stage video instance segmentation[C], 11210-11219(20).

[19] WANG Y Q, XU Z L, WANG X L et al. End-to-end video instance segmentation with transformers[C], 8737-8746(20).

[20] LIU H T, RIVERA SOTO R A, XIAO F Y et al. Yolactedge： real-time instance segmentation on the edge[C], 9579-9585(2021).

[21] BERTASIUS G, TORRESANI L. Classifying， segmenting， and tracking object instances in video with mask propagation[C], 9736-9745(13).

[22] ATHAR A, MAHADEVAN S, OS̆EP A et al[M]. STEm-Seg： Spatio-Temporal Embeddings for Instance Segmentation in Videos, 158-177(2020).

[23] YANG S S, FANG Y X, WANG X G et al. Crossover learning for fast online video instance segmentation[C], 8023-8032(10).

[24] SONG C F, HUANG Y, OUYANG W L et al. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation[C], 3131-3140(15).

[25] HSU CC, HSU KJ, TSAI CC et al. Weakly supervised instance segmentation using the bounding box tightness prior[J]. Advances in Neural Information Processing Systems, 6582-6593(2019).

[26] WANG X G, FENG J P, HU B et al. Weakly-supervised instance segmentation via class-agnostic learning with salient images[C], 10220-10230(20).

[27] TIAN Z, SHEN C H, WANG X L et al. Boxinst： high-performance instance segmentation with box annotations[C], 5439-5448(20).

[28] TIAN Z, SHEN C H, CHEN H[M]. Conditional Convolutions for Instance Segmentation, 282-298(2020).

[29] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 936-944(21).

[30] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(18).

[31] [31] 黄滢， 何自芬， 杨宏宽， 等. 极化自注意力调控的情景式视频实例多尺度分割［J］. 计算机学报， 2022， 45（12）： 2605-2618. doi: 10.11897/SP.J.1016.2022.02605HUANGY， HEZ F， YANGH K， et al. Multi-scale segmentation of episodic video instance through polarized self-attention manipulation［J］. Chinese Journal of Computers， 2022， 45（12）： 2605-2618.（in Chinese）. doi: 10.11897/SP.J.1016.2022.02605

[32] WANG P Q, CHEN P F, YUAN Y et al. Understanding convolution for semantic segmentation[C], 1451-1460(12).