Abstract
The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional DBSCAN by refining the core point definition using an adaptive density threshold. It identifies the “elbow point” in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance.
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The data that support the findings of this study are available from the corresponding author, upon reasonable request.
References
Ali M, Yin B, Kumar A, Sheikh AM et al (2020) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese Control Conference (CCC) (pp 7406–7411). IEEE. https://doi.org/10.23919/CCC50068.2020.9188843
Aslam XD, Hou J, Li Q, Ullah R, Ni Z, Liu Y (2020) Reliable control design for composite-driven scheme based on delay networked T-S fuzzy system. Int J Robust Nonlinear Control 30(4):1622–1642
Chen Z (2019) Observer-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premise. Nonlinear Dyn 95:2923–2941
Chen J, Wang Q, Peng W, Xu H, Li X et al (2022a) Disparity-based multiscale fusion network for transportation detection. IEEE Trans Intell Transp Syst 23(10):18855–18863. https://doi.org/10.1109/TITS.2022.3161977
Chen P, Liu H, **n R, Carval T, Zhao J, **a Y et al (2022b) Effectively detecting operational anomalies in large-scale IoT Data infrastructures by using a GAN-based predictive model. Comput J 65(11):2909–2925. https://doi.org/10.1093/comjnl/bxac085
Chen J, Xu M, Xu W, Li D, Peng W et al (2023) A flow feedback traffic prediction based on visual quantified features. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2023.3269794
Cheng B, Zhu D, Zhao S, Chen J (2016) Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Trans Netw Serv Manage 13(2):349–361. https://doi.org/10.1109/TNSM.2016.2541171
Cheng B, Wang M, Zhao S, Zhai Z, Zhu D et al (2017) Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Trans Netw 25(4):2082–2095. https://doi.org/10.1109/TNET.2017.2705239
Cheng D, Chen L, Lv C, Guo L, Kou Q (2022) Light-guided and cross-fusion U-net for anti-illumination image super-resolution. IEEE Trans Circ Syst Video Technol 32(12):8436–8449. https://doi.org/10.1109/TCSVT.2022.3194169
Cong R, Sheng H, Yang D, Cui Z, Chen R (2023) Exploiting spatial and angular correlations with deep efficient transformers for light field image super-resolution. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2023.3282465
Hazrat B, Yin B, Kumar A, Ali M, Zhang J, Yao J (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27(7):4029–4039. https://doi.org/10.1007/s00500-023-07923-5
Hou X, Zhang L, Su Y, Gao G, Liu Y, Na Z et al (2023) A space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identification. Nano Energy 105:108013. https://doi.org/10.1016/j.nanoen.2022.108013
Jiang S, Zhao C, Zhu Y, Wang C, Du Y, Lei W et al (2022) A practical and economical ultra-wideband base station placement approach for indoor autonomous driving systems. J Adv Transp 2022:1–12. https://doi.org/10.1155/2022/3815306
Kumar A, Shaikh AM, Li Y et al (2021) Pruning filters with L1-norm and capped L1-norm for CNN compression. Appl Intell 51:1152–1160. https://doi.org/10.1007/s10489-020-01894-y
Li J, Zhou N, Sun J, Zhou S, Bai Z, Lu L et al (2022) Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy. Light Sci Appl 11(1):154. https://doi.org/10.1038/s41377-022-00815-7
Li J, Han L, Zhang C, Li Q, Liu Z (2023) Spherical convolution empowered viewport prediction in 360 video multicast with limited FoV feedback. ACM Trans Multimed Comput Commun Appl. https://doi.org/10.1145/3511603
Liang X, Huang Z, Yang S, Qiu L (2018) Device-free motion and trajectory detection via RFID. ACM Trans Embed Comput Syst 17(4):78. https://doi.org/10.1145/3230644
Liu Q, Yuan H, Hamzaoui R, Su H, Hou J et al (2021) Reduced reference perceptual quality model with application to rate control for video-based point cloud compression. IEEE Trans Image Process 30:6623–6636. https://doi.org/10.1109/TIP.2021.3096060
Liu A, Zhai Y, Xu N, Nie W, Li W et al (2022a) Region-aware image captioning via interaction learning. IEEE Trans Circ Syst Video Technol 32(6):3685–3696. https://doi.org/10.1109/TCSVT.2021.3107035
Liu H, Yuan H, Liu Q, Hou J, Zeng H et al (2022b) A hybrid compression framework for color attributes of static 3D point clouds. IEEE Trans Circ Syst Video Technol 32(3):1564–1577. https://doi.org/10.1109/TCSVT.2021.3069838
Lu S, Ban Y, Zhang X, Yang B, Liu S, Yin L, Zheng W (2022) Adaptive control of time delay teleoperation system with uncertain dynamics. Front Neurorobot 16:928863. https://doi.org/10.3389/fnbot.2022.928863
Lu S, Ding Y, Liu M, Yin Z, Yin L et al (2023a) Multiscale feature extraction and fusion of image and text in VQA. Int J Comput Intell Syst 16(1):54. https://doi.org/10.1007/s44196-023-00233-6
Lu S, Liu M, Yin L, Yin Z, Liu X, Zheng W et al (2023b) The multi-modal fusion in visual question answering: a review of attention mechanisms. PeerJ Comput Sci 9:e1400. https://doi.org/10.7717/peerj-cs.1400
Ma X, Dong Z, Quan W, Dong Y, Tan Y (2023) Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: optimal sensor placement and identification algorithm. Mech Syst Signal Process 187:109930. https://doi.org/10.1016/j.ymssp.2022.109930
Muhammad IQ, Majid A, Shamrooz S (2023) Adaptive event-triggered robust H∞ control for Takagi-Sugeno fuzzy networked Markov jump systems with time-varying delay. Asian J Control 25(1):213–228
Shamrooz M, Li Q, Hou J (2021) Fault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473
Shen Y, Ding N, Zheng H-T, Li Y, Yang M (2021) Modeling relation paths for knowledge graph completion. IEEE Trans Knowl Data Eng 33(11):3607–3617. https://doi.org/10.1109/TKDE.2020.2970044
Ullah R, Dai X, Sheng A (2020) Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay. IET Control Theory Appl 14(16):2429–2438
Wang F, Wang H, Zhou X, Fu R (2022) A driving fatigue feature detection method based on multifractal theory. IEEE Sens J 22(19):19046–19059. https://doi.org/10.1109/JSEN.2022.3201015
Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. In: Proceeding of SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T. https://doi.org/10.1117/12.2540362
**ao Y, Konak A (2016) The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transport Res Part E 88:146–166. https://doi.org/10.1016/j.tre.2016.01.011
Xu H, Sun Z, Cao Y et al (2023) A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Comput. https://doi.org/10.1007/s00500-023-09037-4
Yang S, Li Q, Li W, Li X, Liu A (2022a) Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Trans Circuits Syst Video Technol 32(11):8037–8050. https://doi.org/10.1109/TCSVT.2022.3182426
Yang M, Wang H, Hu K, Yin G, Wei Z (2022b) IA-net $: $ an inception–attention-module-based network for classifying underwater images from others. IEEE J Oceanic Eng 47(3):704–717. https://doi.org/10.1109/JOE.2021.3126090
Yao W, Guo Y, Wu Y and Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese Control Conference (CCC) (pp 4192–4197). IEEE. https://doi.org/10.23919/ChiCC.2017.8028015
Yin B, Aslam MS et al (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27:4987–5001. https://doi.org/10.1007/s00500-023-08026-x
Yin B, Khan J, Wang L, Zhang J and Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese Control Conference (CCC) (pp 6772–6777). IEEE. https://doi.org/10.23919/ChiCC.2019.8866334
Zhang H, Luo G, Li J, Wang F-Y (2022) C2FDA: coarse-to-fine domain adaptation for traffic object detection. IEEE Trans Intell Transport Syst 23(8):12633–12647. https://doi.org/10.1109/TITS.2021.3115823
Zheng Y, Lv X, Qian L, Liu X (2022a) An optimal BP neural network track prediction method based on a GA–ACO hybrid algorithm. J Mar Sci Eng 10(10):1399. https://doi.org/10.3390/jmse10101399
Zheng Y, Liu P, Qian L, Qin S, Liu X, Ma Y et al (2022b) Recognition and depth estimation of ships based on binocular stereo vision. J Mar Sci Eng 10:1153. https://doi.org/10.3390/jmse10081153
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Zhang, W. An improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. Soft Comput 27, 18585–18604 (2023). https://doi.org/10.1007/s00500-023-09319-x
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DOI: https://doi.org/10.1007/s00500-023-09319-x