Tunnel Scene Oriented Intelligent Vehicle Radar Vision Cooperative Sensing Research

doi:10.3901/JME.2025.13.080

Abstract

Abstract: Tunnel scenes are characterized by rapid light changes, poor lighting conditions, and noise interference, etc. When the intelligent vehicle senses the tunnel environment, it is prone to omission and error detection, leading to traffic accidents. Therefore, for tunnel scenes, a cooperative perception system and dataset based on the fusion of camera and millimeter-wave radar were constructed, carries out research on the problems of poor camera image quality and loss of details due to sudden changes in illumination at tunnel entrances and exits, and proposes an adaptive exposure control model to adjust the exposure time of the camera. The model analyzes the relationship between the number of feature points of different semantic categories in an image frame as a function of exposure time to ensure that the camera can still image clearly under rapidly changing lighting conditions. In addition, for the vehicle-mounted millimeter-wave radar facing the false target problem caused by multipath echo interference in tunnel scenarios, the multipath propagation theory model is built to analyze the characteristics of potential false targets position and energy attenuation in the radar echo, and the multipath false-target elimination strategy is adopted to eliminate the false interference targets. Finally, the corner-point optical flow estimation of moving targets is introduced in the fusion correlation of camera and millimeter-wave radar to improve the reliability of camera and millimeter-wave radar co-sensing, and a real-vehicle platform is constructed to conduct experiments in a tunnel scenario. The results show that the detection accuracy of the proposed cooperative perception algorithm is increased by 4.8% compared with other models, and it has a better vehicle perception performance in tunnel scenarios, which provides an important guarantee for the safe driving of intelligent vehicles in tunnel environments.

Key words: tunnel autonomous driving, collaborative perception, adaptive exposure, multipath interference, sudden light changes

CLC Number:

TP249

SUN Nianyi, ZHAO Jin, HUANG Lei, WANG Guangwei. Tunnel Scene Oriented Intelligent Vehicle Radar Vision Cooperative Sensing Research[J]. Journal of Mechanical Engineering, 2025, 61(13): 80-95.

References

[1] 李克强. 我看智能网联汽车十年发展[J]. 智能网联汽车，2022(3)：6-9. LI Keqiang. The ten-year development of intelligent connected vehicles[J]. Intelligent Connected Vehicles，2022(3)：6-9.
[2] 戢杨杰，张馨雨，杨紫茹，等. 多智能网联汽车轨迹规划：现状与展望[J]. 机械工程学报，2024，60(10)：129-146. JI Yangjie，ZHANG Xinyu，YANG Ziru，et al. Trajectory planning of multi-intelligent connected vehicles：The State ofthe Art and Perspectives[J]. Journal of Mechanical Engineering，2024，60(10)：129-146.
[3] Jiménez F，Naranjo J E，Anaya J J，et al. Advanced driver assistance system for road environments to improve safety and efficiency[J]. Transportation Research Procedia，2016，14：2245-2254.
[4] 蔡龙成，郑卫华，刘勇. 高速公路隧道安全运营与预警分析[J]. 运输经理世界，2024(9)：149-152. CAI Longcheng，ZHENG Weihua，LIU Yong. Safety Operation and Early Warning Analysis of Highway Tunnel[J]. Transport Business China,2024(9)：149-152.
[5] Tang Z，Hu Y，Wang S，et al. Train post-derailment behaviours and containment methods：a review[J]. Railway Engineering Science，2024，32(1)：59-80.
[6] Yao S，Guan R，Huang X，et al. Radar-camera fusion for object detection and semantic segmentation in autonomous driving：A comprehensive review[J]. IEEE Transactions on Intelligent Vehicles，2023，9(1)：2094-2128.
[7] Minhas S，Li D，Xie Q，Gong X，et al. Automatic defect detection of metro tunnel surfaces using a vision-based inspection system[J]. Advanced Engineering Informatics，2021，47：101206.
[8] Xu Y，Wei S，Bao Y，et al. Automatic seismic damage identification of reinforced concrete columns from images by a region‐based deep convolutional neural network[J]. Structural Control and Health Monitoring，2019，26(3)：e2313.
[9] Wang J，Ren Y，Ye Z，et al. LRE-Net：A vision-based real-time rail extraction method under Metro tunnel low-light scene[J]. Measurement，2023，221：113567.
[10] Li C，Xu P，Niu L，et al. Tunnel crack detection using coarse-to-fine region localization and edge detection[J]. Wiley Interdisciplinary Reviews：Data Mining and Knowledge Discovery，2019，9(5)：e1308.
[11] Zhu K，Chen S，Shi J，et al. Localization of autonomous vehicles in tunnels based on roadside multi-sensor fusion[J]. IEEE Transactions on Intelligent Vehicles，2024：1-13.
[12] STRAUSS A，BIEN J，NEUNER H，et al. Sensing and monitoring in tunnels testing and monitoring methods for the assessment of tunnels[J]. Structural concrete，2020，21(4)：1356-1376.
[13] ZHOU H，ZHOU S. Scene categorization towards urban tunnel traffic by image quality assessment[J]. Journal of Visual Communication and Image Representation，2019，65：102655.
[14] Minhas S，Hernández-Sabaté A，Ehsan S，et al. Effects of non-driving related tasks during self-driving mode[J]. IEEE Transactions On Intelligent Transportation Systems，2020，23(2)：1391-1399.
[15] Li Y，Wei Y ，Wang Y，et al. False detections revising algorithm for millimeter wave radar SLAM in tunnel[J]. Remote Sensing，2023，15(1)：277.
[16] Rao B S. Dynamic histogram equalization for contrast enhancement for digital images[J]. Applied Soft Computing，2020，89：106114-106126.
[17] Huang C，Li X，Wen Y. AN OTSU image segmentation based on fruitfly optimization algorithm[J]. Alexandria Engineering Journal，2021，60(1)：183-188.
[18] Han D，Li L，Guo X，et al. Multi-exposure image fusion via deep perceptual enhancement[J]. Information Fusion，2022，79：248-262.
[19] Tomasi J，Wagstaff B，Waslander S L，et al. Learned camera gain and exposure control for improved visual feature detection and matching[J]. IEEE Robotics and Automation Letters，2021，6(2)：2028-2035.
[20] Onzon E，Mannan F，Heide F. Neural auto-exposure for high-dynamic range object detection[C]// In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：7710-7720.
[21] Duan Y，Qiu S，Jin W，et al. High-speed rail tunnel panoramic inspection image recognition technology based on improved YOLOv5[J]. Sensors，2023，23(13)：5986-6005.
[22] Gor H，Yang Bin. High-performance automotive radar：A review of signal processing algorithms and modulation schemes[J]. IEEE Signal Processing Magazine，2019，36(5)：32-44.
[23] MISHRA K V，SHANKAR M R B，KOIVUNEU V，et al. Toward millimeter-wave joint radar communications：A signal processing perspective[J]. IEEE Signal Processing Magazine，2019，36(5)：100-114.
[24] ZAHARISA Z D，GRAVAS I P，LAZARIDIS P I，et al. An effective modification of conventional beamforming methods suitable for realistic linear antenna arrays[J]. IEEE Transactions on Antennas and Propagation，2020，68(7)：5269-5279.
[25] Roos F，Sadeghi M，Bechter J，et al. Ghost target identification by analysis of the Doppler distribution in automotive scenarios[C]//201718th International Radar Symposium (IRS)，2017：1-9.
[26] Lekic V，Babic Z. Automotive radar and camera fusion using generative adversarial networks[J]. Computer Vision and Image Understanding，2019，184：1-8.
[27] Wang Z，Miao X，Huang Z，et al. Research of target detection and classification techniques using millimeter-wave radar and vision sensors[J]. Remote Sensing，2021，13(6)：1064-1086.
[28] Shi J，Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2000，22(8)：888-905.
[29] KRIZHEVSKY A，SUTSKEVER I，HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Association for Computing Machinery，2017，60(6)：84-90.
[30] HINTON G E，SALAKHUTDINOV R R. Replicated Softmax：an undirected topic model[C]//Proceedings of the 22nd International Conference on Neural Information Processing Systems，2009：1607-1614.
[31] Shao W，Qian Z. A new partially adaptive minimum variance distortionless response beamformer with constrained stability least mean squares algorithm[J]. Advanced Science Letters，2013，19(4)：1071-1074.
[32] SHAHED M，KENANE E，KHALFA A,et al. Djahli. Exact closed-form Pfa expressions for CA- and GO-CFAR detectors in gamma-distributed radar clutter[J]. IEEE Transactions on Aerospace and Electronic Systems，2023，59(4)：4674-4679.
[33] Akhtar J. Training of neural network target detectors mentored by SO-CFAR[C]//202028th European Signal Processing Conference (EUSIPCO)，Amsterdam，Netherlands，2021：1522-1526.
[34] Ravindra B，Mallik A. Performance of non-linear vibration isolators under harmonic excitation[J]. Journal of Sound and Vibration，1994，170(3)：325-337.
[35] 曾世杰，刘景萍，马静. 基于FPGA的毫米波调频引信信号处理设计及实现[J]. 微波学报，2021，37(增刊1)：154-157. ZENG S J，LIU J P，MA J. Design and Implementation of Signal Processing for Millimeter Wave FMMFuze Based on FPGA[J]. Journal of Microwaves,2021，37(Suppl.1)：154-157.
[36] Bansal M，Kumar M，Kumar M，et al. An efficient technique for object recognition using Shi-Tomasi corner detection algorithm[J]. Soft Computing，2021，25(6)：4423-4432.
[37] Winkler J R. Error analysis and condition estimation of the pyramidal form of the Lucas-Kanade method in optical flow[J]. Electronics，2024，13(5)：812-830.
[38] Duan K，Bai S，Xie L，et al. Centernet：Keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：6569-6578.
[39] Farag W. Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles[J]. Proceedings of the Institution of Mechanical Engineers，Part I：Journal of Systems and Control Engineering，2021，235(7)：1125-1138.
[40] Chen Y，Zheng W，Zhao Y，et al. Dw-yolo：An efficient object detector for drones and self-driving vehicles[J]. Arabian Journal for Science and Engineering，2023，48(2)：1427-1436.