ijastech International Journal of Automotive Science And Technology 2587-0963 Otomotiv Mühendisleri Derneği 10.30939/ijastech..1845650 Automotive Engineering (Other) Otomotiv Mühendisliği (Diğer) A Review on Fundamentals, Applications, Challenges and Current Status of Spiking Automotive Electronics https://orcid.org/0000-0002-7747-7777 Dikmen İsmail Can ISTINYE UNIVERSITY 04 16 2026 10 2 281 305 12 19 2025 04 13 2026 Copyright © 2016, International Journal of Automotive Science And Technology 2016 International Journal of Automotive Science And Technology

Automotive edge systems face a growing gap between computational demand and what vehicle platforms can supply under tight power and thermal budgets, especially in autonomous vehicles. Neuromorphic computing is proposed as response, owing to its event driven operation. But earlier reviews on this subject tend to mix demonstrated uses with speculative applications and do not always relate efficiency claims to real driving conditions. This review addresses this gap in the literature; automotive system integration of neuromorphic hardware, spiking neural network training and deployment, event based sensing. Reviewed studies are separated into demonstrated implementations with measurable outcomes on stated platform and proposed opportunities that still lack automotive grade validation. Four observations are obtained from this review. First, efficiency gains from spike based processing become credible mainly when the workload is sparse and temporal by nature and when coding policy is selected with bounded time to decision in mind. Second, cross study comparison remains difficult because latency, energy, event rate condition, and stopping rule are usually reported in inconsistent ways across published studies. Third, deployment barriers are largely procedural, including toolchain maturity, integration of asynchronous accelerators with synchronous ECU timing, and the construction of safety arguments under ISO 26262 and SOTIF. Fourth, public industrial activity is still concentrated on bounded functions such as driver monitoring, keyword spotting, and radar pre processing rather than full neuromorphic autonomy stacks. Based on these findings, a deployment roadmap is proposed around always on modules with explicit timing contracts, automotive grade benchmark suites, and safety case patterns that constrain learning and enforce monitorable behavioral contracts.

 ADAS automotive electronics edge AI event-based vision functional safety neuromorphic computing spiking neural networks. [1] Mead C. Neuromorphic electronic systems. Proc IEEE. 1990;78(10):1629-1636. https://doi.org/10.1109/5.58356 [2] Halaly R, Ezra Tsur E. Autonomous driving controllers with neuromorphic spiking neural networks. Front Neurorobot. 2023;17:1234962. https://doi.org/10.3389/fnbot.2023.1234962 [3] Moitra A, Bhattacharjee A, Kuang R, Panda P. SpikeSim: An end-to-end compute-in-memory hardware evaluation tool for benchmarking spiking neural networks. IEEE Trans Comput-Aided Des Integr Circuits Syst. 2024;43(1):90-103. https://doi.org/10.1109/TCAD.2023.3274918 [4] Shariff W, Dilmaghani MS, Kielty P, Moustafa M, Lemley J, Corcoran P. Event cameras in automotive sensing: A review. IEEE Access. 2024;12:51275-51306. https://doi.org/10.1109/ACCESS.2024.3386032 [5] Man Y, Xie L, Qiao S, Zhou Y, Shang D. Differentiable ar-chitecture search with multi-dimensional attention for spiking neural networks. Neurocomputing. 2024;601:128181. https://doi.org/10.1016/j.neucom.2024.128181 [6] Sanyal S, Joshi A, Nagaraj M, Manna RK, Roy K. Energy-efficient autonomous aerial navigation with dynamic vision sensors: A physics-guided neuromorphic approach. CoRR. 2025;abs/2502.05938. https://doi.org/10.48550/arXiv.2502.05938 [7] Lee HJ, Lim JH. Adaptive synaptic adjustment mechanism to improve learning performances of spiking neural networks. Comput Intell. 2024;40(5):e70001. https://doi.org/10.1111/coin.70001 [8] Du Z, Chen Y, Zhang L, Zhou S, Zhi T. TDSNN: From deep neural networks to deep spike neural networks with temporal-coding. In: Proc AAAI Conf Artif Intell. 2019;33:1319-1326. https://doi.org/10.1609/aaai.v33i01.33011319 [9] Maass W. Networks of spiking neurons: The third generation of neural network models. Neural Netw. 1997;10(9):1659-1671. https://doi.org/10.1016/S0893-6080(97)00011-7 [10] Pfeiffer M, Pfeil T. Deep learning with spiking neurons: Op-portunities and challenges. Front Neurosci. 2018;12:774. https://doi.org/10.3389/fnins.2018.00774 [11] Thorpe S, Delorme A, Van Rullen R. Spike-based strategies for rapid processing. Neural Netw. 2001;14(6-7):715-725. https://doi.org/10.1016/S0893-6080(01)00083-1 [12] Gerstner W, Kistler WM. Spiking Neuron Models: Single Neu-rons, Populations, Plasticity. Cambridge University Press; 2002. https://doi.org/10.1017/CBO9780511815706 [13] Izhikevich EM. Simple model of spiking neurons. IEEE Trans Neural Netw. 2003;14(6):1569-1572. https://doi.org/10.1109/TNN.2003.820440 [14] Gallego G, Delbruck T, Orchard G, Bartolozzi C, Taba B, Censi A, et al. Event-based vision: A survey. IEEE Trans Pat-tern Anal Mach Intell. 2022; 44(1):154-180. https://doi.org/10.1109/TPAMI.2020.3008413 [15] Neftci EO, Mostafa H, Zenke F. Surrogate gradient learning in spiking neural networks. IEEE Signal Process Mag. 2019;36(6):51-63. https://doi.org/10.1109/MSP.2019.2931595 [16] Bi GQ, Poo MM. Synaptic modifications in cultured hippo-campal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci. 1998;18(24):10464-10472. https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998 [17] Song S, Miller KD, Abbott LF. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neu-rosci. 2000;3(9):919-926. https://doi.org/10.1038/78829 [18] Merolla PA, Arthur JV, Alvarez-Icaza R, Cassidy AS, Sawada J, Akopyan F, et al. A million spiking-neuron integrated cir-cuit with a scalable communication network and interface. Science. 2014;345(6197):668-673. https://doi.org/10.1126/science.1254642 [19] Isik MC, Tiwari K, Eryilmaz MB, Dikmen IC. Accelerating sensor fusion in neuromorphic computing: A case study on Loihi-2. In: 2024 IEEE HPEC. IEEE; 2024. p. 1-7. https://doi.org/10.1109/HPEC62836.2024.10938501 [20] Davies M, Srinivasa N, Lin TH, Chinya G, Cao Y, Choday SH, et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro. 2018;38(1):82-99. https://doi.org/10.1109/MM.2018.112130359 [21] Patel H, Vanarse A, Carlson KD, Osseiran A. Bringing touch to the edge: A neuromorphic processing approach for event-based tactile systems. In: 2023 IEEE 5th AICAS. 2023. p. 1-5. https://doi.org/10.1109/AICAS57966.2023.10168592 [22] Mayr C, Hoppner S, Furber S. SpiNNaker 2: A 10 million core processor system for brain simulation and machine learning — keynote presentation. In: Communicating Process Architectures 2017 & 2018. Concurrent Systems Engineering Series, Vol. 70. IOS Press; 2019. p.277-280. https://doi.org/10.3233/978-1-61499-949-2-277 [23] Pehle C, Billaudelle S, Cramer B, Kaiser J, Schreiber K, Stradmann Y, et al. The BrainScaleS-2 accelerated neuromor-phic system with hybrid plasticity. Front Neurosci. 2022;16:795876. https://doi.org/10.3389/fnins.2022.795876 [24] Balaji A, Catthoor F, Das A, Wu Y, Huynh K, Dell'Anna FG, et al. Mapping spiking neural networks to neuromorphic hardware. IEEE Trans Very Large Scale Integr (VLSI) Syst. 2020;28(1):76-86. https://doi.org/10.1109/TVLSI.2019.2951493 [25] Lin CK, Wild A, Chinya GN, Cao Y, Davies M, Lavery DM, et al. Programming spiking neural networks on Intel's Loihi. Computer. 2018;51(3):52-61. https://doi.org/10.1109/MC.2018.157113521 [26] Bouvier M, Valentian A, Mesquida T, Rummens F, Reyboz M, Vianello E, et al. Spiking neural networks hardware imple-mentations and challenges: A survey. J Emerg Technol Com-put Syst. 2019;15(2):22. https://doi.org/10.1145/3304103 [27] Kulkarni SR, Parsa M, Mitchell JP, Schuman CD. Benchmark-ing the performance of neuromorphic and spiking neural network simulators. Neurocomputing. 2021;447:145-160. https://doi.org/10.1016/j.neucom.2021.03.028 [28] Tang F, Gao W. SNNBench: End-to-end AI-oriented spiking neural network benchmarking. BenchCouncil Trans Bench-marks Stand Eval. 2023;3(1):100108. https://doi.org/10.1016/j.tbench.2023.100108 [29] Matinizadeh S, Das A. An open-source and extensible framework for fast prototyping and benchmarking of spiking neural network hardware. In: 2024 34th FPL. 2024. p. 250-256. https://doi.org/10.1109/FPL64840.2024.00042 [30] Waschle M, Thaler F, Berres A, Polzlbauer F, Albers A. A review on AI safety in highly automated driving. Front Artif Intell. 2022;5:952773. https://doi.org/10.3389/frai.2022.952773 [31] Van Rullen R, Thorpe SJ. Rate coding versus temporal order coding: What the retinal ganglion cells tell the visual cortex. Neural Comput. 2001;13(6):1255-1283. https://doi.org/10.1162/08997660152002852 [32] Park S, Kim SJ, Na B, Yoon S. T2FSNN: Deep spiking neural networks with time-to-first-spike coding. In: 2020 57th ACM/IEEE DAC. 2020. https://doi.org/10.1109/DAC18072.2020.9218689 [33] Im J, Kim J, Yoo HN, Baek JW, Kwon D, Oh S, et al. On-chip trainable spiking neural networks using time-to-first-spike encoding. IEEE Access. 2022;10:31263-31272. https://doi.org/10.1109/ACCESS.2022.3160271 [34] Zhang Z, Liu Q. Spike-event-driven deep spiking neural net-work with temporal encoding. IEEE Signal Process Lett. 2021;28:484-488. https://doi.org/10.1109/LSP.2021.3059172 [35] Ma C, Yu Q. AugMapping: Accurate and efficient inference with deep double-threshold spiking neural networks. In: 2020 IEEE SSCI. 2020. p. 2002-2007. https://doi.org/10.1109/SSCI47803.2020.9308402 [36] Chowdhury SS, Garg I, Roy K. Spatio-temporal pruning and quantization for low-latency spiking neural networks. In: IJCNN 2021. 2021. https://doi.org/10.1109/IJCNN52387.2021.9534111 [37] Hu Y, Zheng Q, Jiang X, Pan G. Fast-SNN: Fast spiking neu-ral network by converting quantized ANN. IEEE Trans Pat-tern Anal Mach Intell. 2023; 45(12):14546-14562. https://doi.org/10.1109/TPAMI.2023.3275769 [38] Hu Y, Zheng Q, Pan G. BitSNNs: Revisiting energy-efficient spiking neural networks. IEEE Trans Cogn Dev Syst. 2024;16(5):1736-1747. https://doi.org/10.1109/TCDS.2024.3383428 [39] Meng Q, Xiao M, Yan S, Wang Y, Lin Z, Luo ZQ. Towards memory- and time-efficient backpropagation for training spiking neural networks. In: 2023 IEEE/CVF ICCV. 2023. p. 8446-8455. https://doi.org/10.1109/ICCV51070.2023.00776 [40] Nomura O, Sakemi Y, Hosomi T, Morie T. Robustness of spiking neural networks based on time-to-first-spike encoding against adversarial attacks. IEEE Trans Circuits Syst II Ex-press Briefs. 2022;69(9):3640-3644. https://doi.org/10.1109/TCSII.2022.3184313 [41] Olin-Ammentorp W, Beckmann K, Schuman CD, Plank JS, Cady NC. Stochasticity and robustness in spiking neural net-works. Neurocomputing. 2021;419:23-36. https://doi.org/10.1016/j.neucom.2020.07.105 [42] Cao Y, Chen Y, Khosla D. Spiking deep convolutional neural networks for energy-efficient object recognition. Int J Com-put Vis. 2015;113(1):54-66. https://doi.org/10.1007/s11263-014-0788-3 [43] Hunsberger E, Eliasmith C. Spiking deep networks with LIF neurons. arXiv preprint arXiv:1510.08829. 2015. https://doi.org/10.48550/arXiv.1510.08829 [44] Rueckauer B, Lungu IA, Hu Y, Pfeiffer M, Liu SC. Conver-sion of continuous-valued deep networks to efficient event-driven networks for image classification. Front Neurosci. 2017; 11:682. https://doi.org/10.3389/fnins.2017.00682 [45] Bu T, Fang W, Ding J, Dai P, Yu Z, Huang T. Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks. In: ICLR. 2022. [46] Rathi N, Srinivasan G, Panda P, Roy K. Enabling deep spik-ing neural networks with hybrid conversion and spike timing dependent backpropagation. In: ICLR. 2020. [47] Lee JH, Delbruck T, Pfeiffer M. Training deep spiking neural networks using backpropagation. Front Neurosci. 2016; 10:508. https://doi.org/10.3389/fnins.2016.00508 [48] Shrestha SB, Orchard G. SLAYER: Spike layer error reas-signment in time. In: NeurIPS. 2018; 31:1419-1428. [49] Fang W, Yu Z, Chen Y, Huang T, Masquelier T, Tian Y. Deep residual learning in spiking neural networks. In: NeurIPS. 2021; 34:21056-21069. [50] Kudithipudi D, Schuman C, Vineyard CM, et al. Neuromor-phic computing at scale. Nature. 2025;637(8047):801-812. https://doi.org/10.1038/s41586-024-08253-8 [51] Parpart GG, Risbud SR, Kenyon GT, Watkins YZ. Imple-menting and benchmarking the locally competitive algorithm on the Loihi 2 neuromorphic processor. In: ICONS 2023. 2023. Article 15. https://doi.org/10.1145/3589737.3605973 [52] Fang W, Chen Y, Ding J, Yu Z, Masquelier T, Chen D, et al. SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. Sci Adv. 2023;9(40):eadi1480. https://doi.org/10.1126/sciadv.adi1480 [53] Narduzzi S, Mateu L, Jokic P, Azarkhish E, Dunbar LA. Benchmarking neuromorphic computing for inference. In: Industrial Artificial Intelligence Technologies and Applica-tions. River Publishers; 2023. p. 1-20. [54] Chen G, Cao H, Conradt J, Tang H, Rohrbein F, Knoll A. Event-based neuromorphic vision for autonomous driving: A paradigm shift for bio-inspired visual sensing and perception. IEEE Signal Process Mag. 2020;37(4):34-49. https://doi.org/10.1109/MSP.2020.2985815 [55] Gehrig M, Aarents W, Gehrig D, Scaramuzza D. DSEC: A stereo event camera dataset for driving scenarios. IEEE Robot Autom Lett. 2021;6(3):4947-4954. https://doi.org/10.1109/LRA.2021.3068942 [56] Alonso I, Murillo AC. EV-SegNet: Semantic segmentation for event-based cameras. In: 2019 IEEE/CVF CVPRW. 2019. p. 1624-1633. https://doi.org/10.1109/CVPRW.2019.00205 [57] Jiang J, Zhou B, Zhou T, Zhong Y. Deep event-based object detection in autonomous driving: A survey. In: 2024 10th BigDIA. 2024. p. 447-454. https://doi.org/10.1109/BigDIA63733.2024.10808654 [58] She X, Mukhopadhyay S. SPEED: Spiking neural network with event-driven unsupervised learning and near-real-time inference for event-based vision. IEEE Sens J. 2021; 21(18):20578-20591. https://doi.org/10.1109/JSEN.2021.3098013 [59] Hasssan A, Meng J, Seo JS. Spiking neural network with learnable threshold for event-based classification and object detection. In: 2024 IJCNN. 2024. p. 1-8. https://doi.org/10.1109/IJCNN60899.2024.10650320 [60] Kosta AK, Roy K. Adaptive-SpikeNet: Event-based optical flow estimation using spiking neural networks with learnable neuronal dynamics. In: 2023 IEEE ICRA. 2023. p. 6021-6027. https://doi.org/10.1109/ICRA48891.2023.10160551 [61] Zhang Y, Lv H, Zhao Y, Feng Y, Liu H, Bi G. Event-based optical flow estimation with spatio-temporal backpropagation trained spiking neural network. Micromachines. 2023;14(1):203. https://doi.org/10.3390/mi14010203 [62] Viale A, Marchisio A, Martina M, Masera G, Shafique M. CarSNN: An efficient spiking neural network for event-based autonomous cars on the Loihi neuromorphic research proces-sor. In: 2021 IJCNN. 2021. https://doi.org/10.1109/IJCNN52387.2021.9533738 [63] Viale A, Marchisio A, Martina M, Masera G, Shafique M. LaneSNNs: Spiking neural networks for lane detection on the Loihi neuromorphic processor. In: 2022 IEEE/RSJ IROS. 2022. p. 79-86. https://doi.org/10.1109/IROS47612.2022.9981034 [64] Safa A, Keuninckx L, Gielen G, Catthoor F. Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems. Springer; 2024. https://doi.org/10.1007/978-3-031-63565-6 [65] Safa A, Verbelen T, Ocket I, Bourdoux A, Sahli H, Catthoor F, et al. Fusing event-based camera and radar for SLAM using spiking neural networks with continual STDP learning. In: 2023 IEEE ICRA. 2023. p. 2782-2788. https://doi.org/10.1109/ICRA48891.2023.10160681 [66] Isik MC, Vishwamith H, Sur Y, Inadagbo K, Dikmen IC. NeuroSec: FPGA-based neuromorphic audio security. In: ARC 2024. Springer; 2024. p. 134-147. https://doi.org/10.1007/978-3-031-55673-9_10 [67] Lopez-Randulfe J, Reeb N, Karimi N, Liu C, Gonzalez HA, Dietrich R, et al. Time-coded spiking Fourier transform in neuromorphic hardware. IEEE Trans Comput. 2022;71(11):2792-2802. https://doi.org/10.1109/TC.2022.3162708 [68] Lopez-Randulfe J, Duswald T, Bing Z, Knoll AC. Spiking neural network for Fourier transform and object detection for automotive radar. Front Neurorobot. 2021;15:688344. https://doi.org/10.3389/fnbot.2021.688344 [69] Reeb N, Lopez-Randulfe J, Dietrich R, Knoll AC. Range and angle estimation with spiking neural resonators for FMCW ra-dar. Neuromorphic Comput Eng. 2025;5(2):024009. https://doi.org/10.1088/2634-4386/adcf46 [70] Venon A, Dupuis Y, Vasseur P, Merriaux P. Millimeter wave FMCW radars for perception, recognition and localization in automotive applications: A survey. IEEE Trans Intell Veh. 2022;7(3):533-555. https://doi.org/10.1109/TIV.2022.3167733 [71] Zhuang G, Bing Z, Huang K, Knoll A. Toward neuromorphic perception: Spike-driven lane segmentation for autonomous driving using LiDAR sensor. In: 2023 IEEE 26th ITSC. 2023. p. 2448-2453. https://doi.org/10.1109/ITSC57777.2023.10422133 [72] Roriz R, Tumnark P, Gomes T, Costeira JP, Bernardino A, Fonseca JR. Automotive LiDAR technology: A survey. IEEE Trans Intell Transp Syst. 2022; 23(7):6282-6297. https://doi.org/10.1109/TITS.2021.3086804 [73] Kielty P, Dilmaghani MS, Shariff W, Ryan C, Lemley J, Cor-coran P. Neuromorphic driver monitoring systems: A proof-of-concept for yawn detection and seatbelt state detection us-ing an event camera. IEEE Access. 2023; 11:96363-96373. https://doi.org/10.1109/ACCESS.2023.3312190 [74] Chung HJ, Kang B, Yang YS. N-DriverMotion: Driver motion learning and prediction using an event-based camera and di-rectly trained spiking neural networks on Loihi 2. IEEE Open J Veh Technol. 2025; 6:68-80. https://doi.org/10.1109/OJVT.2024.3504481 [75] Iddrisu K, Shariff W, Corcoran P, O'Connor NE, Lemley J, Little S. Event camera-based eye motion analysis: A survey. IEEE Access. 2024; 12:136783-136804. https://doi.org/10.1109/ACCESS.2024.3462109 [76] Jiang Y, Wang W, Yu L, He C. Eye tracking based on event camera and spiking neural network. Electronics. 2024;13(14):2879. https://doi.org/10.3390/electronics13142879 [77] Friedrichs F, Yang B. Camera-based drowsiness reference for driver state classification under real driving conditions. In: 2010 IEEE Intelligent Vehicles Symposium. 2010. p. 101-106. https://doi.org/10.1109/IVS.2010.5548039 [78] Ghourabi A, Ghazouani H, Barhoumi W. Driver drowsiness detection based on joint monitoring of yawning, blinking and nodding. In: 2020 IEEE 16th ICCP. 2020. p. 407-414. https://doi.org/10.1109/ICCP51029.2020.9266160 [79] Chen X, Su L, Zhao J, Qiu K, Jiang N, Zhai G. Sign language gesture recognition and classification based on event camera with spiking neural networks. Electronics. 2023;12(4):786. https://doi.org/10.3390/electronics12040786 [80] Xing Y, Di Caterina G, Soraghan J. A new spiking convolu-tional recurrent neural network (SCRNN) with applications to event-based hand gesture recognition. Front Neurosci. 2020;14:590164. https://doi.org/10.3389/fnins.2020.590164 [81] Liu Q, Xing D, Tang H, Ma D, Pan G. Event-based action recognition using motion information and spiking neural net-works. In: IJCAI-21. 2021. p. 1743-1749. https://doi.org/10.24963/ijcai.2021/240 [82] Lei J, Cheng Y, Wang J, Xu L, Zhang J, Li Z. In-vehicle acoustic event detection model based on deep neural network. In: 2023 ICCSI. 2023. p. 503-508. https://doi.org/10.1109/ICCSI58851.2023.10304030 [83] Antony A, Theimer W, Grossetti G, Friedrich CM. Acoustic event detection in vehicles: A multi-label classification ap-proach. Sensors. 2025;25(8):2591. https://doi.org/10.3390/s25082591 [84] Yoo J, Lee CH, Jea HM, Lee SK, Yoon Y, Lee J, et al. Classi-fication of road surfaces based on CNN architecture and tire acoustical signals. Appl Sci. 2022;12(19):9521. https://doi.org/10.3390/app12199521 [85] Park J, Min K, Kim H, Lee W, Cho G, Huh K. Road surface classification using a deep ensemble network with sensor fea-ture selection. Sensors. 2018;18(12):4342. https://doi.org/10.3390/s18124342 [86] Li H, Liu F, Zhao Z, Karimzadeh M. Effective safety message dissemination with vehicle trajectory predictions in V2X net-works. Sensors. 2022;22(7):2686. https://doi.org/10.3390/s22072686 [87] Khalid I, Maglogiannis V, Naudts D, Shahid A, Moerman I. Optimizing hybrid V2X communication: An intelligent tech-nology selection algorithm using 5G, C-V2X PC5 and DSRC. Future Internet. 2024;16(4):107. https://doi.org/10.3390/fi16040107 [88] Dai X, Zhou R, Zhang J, He K, Lin F, Ma H. SocNet: A phys-ics-guided neural network for battery state-of-charge estima-tion robust to temperature variations and sensor noises. IEEE Trans Transp Electrification. 2025;11(5):11165-11176. https://doi.org/10.1109/TTE.2025.3573618 [89] How DNT, Hannan MA, Lipu MSH, Sahari KSM, Ker PJ, Muttaqi KM. State-of-charge estimation of Li-ion battery in electric vehicles: A deep neural network approach. IEEE Trans Ind Appl. 2020;56(5):5565-5574. https://doi.org/10.1109/TIA.2020.3004294 [90] Wei M, Ye M, Li JB, Wang Q, Xu X. State of charge estima-tion of lithium-ion batteries using LSTM and NARX neural networks. IEEE Access. 2020;8:189236-189245. https://doi.org/10.1109/ACCESS.2020.3031340 [91] Dikmen IC, Ekici YE, Karadag T. Multi-chemistry battery management system for electric vehicles. Eur J Res Dev. 2022;2(4):32-49. https://doi.org/10.56038/ejrnd.v2i4.115 [92] Ekici YE, Dikmen IC, Nurmuhammed M, Karadag T. Effi-ciency analysis of various batteries with real-time data on a hybrid electric vehicle. Int J Automot Sci Technol. 2021;5(3):214-223. https://doi.org/10.30939/ijastech..946047 [93] Wang Y, Han X, Guo D, Lu L, Chen Y, Ouyang M. Physics-informed recurrent neural network with fractional-order gra-dients for state-of-charge estimation of lithium-ion battery. IEEE J Radio Freq Identif. 2022; 6:968-971. https://doi.org/10.1109/JRFID.2022.3211841 [94] Avanthika DSRSL, Udumula RR, Lokeshgupta B, Morampudi MK. Dual estimation of state of charge and state of health of a battery: Leveraging machine learning and deep neural net-works. In: 2025 4th ICPC2T. 2025. p. 1-6. https://doi.org/10.1109/ICPC2T63847.2025.10958585 [95] Dikmen IC. Neuromorphic battery management system (nBMS). Turkish Patent Application 2025/018585. 2025. [96] Dikmen IC, Karadag T. Electrical method for battery chemi-cal composition determination. IEEE Access. 2022; 10:6496-6504. https://doi.org/10.1109/ACCESS.2022.3143040 [97] Dikmen IC, Ari A, Karadag T. Physics-informed LSTM and EKF fusion for robust battery SoC estimation. Turkish J Sci Technol. 2026;21(1):57-67. https://doi.org/10.55525/tjst.1812986. [98] Dikmen IC. Adaptive, modular and intelligent battery man-agement system capable of automatically determining battery chemistry. Turkish Patent TR 2021 018973. 2021. [99] International Organization for Standardization. Road vehicles - Functional safety. ISO 26262:2018. Geneva: ISO; 2018. [100] International Organization for Standardization. Road vehicles - Safety of the intended functionality. ISO/PAS 21448:2019. Geneva: ISO; 2019. [101] Calaim N, Dehmelt FA, Goncalves PJ, Machens CK. The geometry of robustness in spiking neural networks. eLife. 2022;11:e73276. https://doi.org/10.7554/eLife.73276 [102] Beland S, Chang I, Chen A, Moser M, Paunicka JL, Stuart D, et al. Towards assurance evaluation of autonomous systems. In: 2020 IEEE/ACM ICCAD. 2020. p. 1-6. [103] Kuznietsov A, Gyevnar B, Wang C, Peters S, Albrecht SV. Explainable AI for safe and trustworthy autonomous driving: A systematic review. IEEE Trans Intell Transp Syst. 2024;25(12):19342-19364. https://doi.org/10.1109/TITS.2024.3474469 [104] Peng L, Li B, Yu W, Yang K, Shao W, Wang H. SOTIF entro-py: Online SOTIF risk quantification and mitigation for au-tonomous driving. IEEE Trans Intell Transp Syst. 2024;25(2):1530-1546. https://doi.org/10.1109/TITS.2023.3322166 [105] Katz G, Barrett C, Dill DL, Julian K, Kochenderfer MJ. Relu-plex: An efficient SMT solver for verifying deep neural net-works. In: CAV. Springer; 2017. p. 97-117. https://doi.org/10.1007/978-3-319-63387-9_5 [106] Zhang H, Cheng J, Zhang J, Liu H, Wei Z. A regularization perspective based theoretical analysis for adversarial robust-ness of deep spiking neural networks. Neural Netw. 2023;165:164-174. https://doi.org/10.1016/j.neunet.2023.05.038 [107] Liu Y, Bu T, Ding J, Hao Z, Huang T, Yu Z. Enhancing ad-versarial robustness in SNNs with sparse gradients. In: Proc 41st ICML (ICML'24). JMLR.org; 2024. Article 1239. [108] van der Perk P. A Distributed Safety Mechanism for Auton-omous Vehicle Software Using Hypervisors [PhD thesis]. Eindhoven University of Technology; 2019. [109] Yik J, Van den Berghe K, den Blanken D, et al. The Neuro-Bench framework for benchmarking neuromorphic compu-ting algorithms and systems. Nat Commun. 2025;16(1):1545. https://doi.org/10.1038/s41467-025-56739-4 [110] Smolka M, Stoepel L, Quill J, Wahlbrink T, Floehr J, Boschen S, et al. Transdisciplinary development of neuromorphic computing hardware for artificial intelligence applications. In: Transformation Towards Sustainability. Springer; 2024. p. 271-301. https://doi.org/10.1007/978-3-031-54700-3_10 [111] Isik MC, Naoukin J, Dikmen IC. NeuroVM: Dynamic neuro-morphic hardware virtualization. In: 2024 IEEE 15th IGSC. IEEE; 2024. p. 74-79. https://doi.org/10.1109/IGSC64514.2024.00023 [112] Finateu T, Niwa A, Matolin D, Tsuchimoto K, Mascheroni A, Reynaud E, et al. A 1280x720 back-illuminated stacked tem-poral contrast event-based vision sensor with 4.86um pixels, 1.066GEPS readout, programmable event-rate controller and compressive data-formatting pipeline. In: 2020 IEEE ISSCC. 2020. p. 112-114. https://doi.org/10.1109/ISSCC19947.2020.9063149 [113] Jung S, Kim SJ. MRAM in-memory computing macro for AI computing. In: 2022 IEDM. 2022. p. 33.4.1-33.4.4. https://doi.org/10.1109/IEDM45625.2022.10019461 [114] Kim Y, Chough J, Panda P. Beyond classification: Directly training spiking neural networks for semantic segmentation. Neuromorphic Comput Eng. 2022;2(4):044015. https://doi.org/10.1088/2634-4386/ac9b86