{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T18:05:36Z","timestamp":1784829936042,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Trajectory prediction is a critical component of autonomous driving systems, enabling vehicles to anticipate future motion behaviors and perform safe decision-making in dynamic traffic environments. While recent trajectory forecasting methods achieve state-of-the-art prediction accuracy, many operate as black-box systems and are evaluated primarily on high-end computing platforms, limiting their interpretability and practical deployment feasibility in resource-constrained autonomous driving systems. To address these limitations, this work proposes an interpretable and edge-deployable spatio-temporal trajectory prediction framework for autonomous driving. The proposed architecture integrates a Temporal Convolutional Network with Multi-Head Self-Attention (TCN\u2013MHSA) in ActorNet for selective temporal modeling, a Lane Graph Attention Network (LaneGAT) for structured spatial reasoning, and a multi-stage FusionNet for actor\u2013lane interaction. To improve model interpretability, a comprehensive Explainable AI (XAI) evaluation framework is introduced, including temporal sensitivity analysis, interaction-aware perturbation studies, spatial influence analysis, and gradient-based feature attribution methods. These analyses provide insights into how the model captures temporal motion dependencies, neighboring vehicle interactions, and environmental context during trajectory prediction. To improve the robustness of the interpretability analysis, temporal sensitivity was additionally evaluated over 100 validation scenes, demonstrating that recent observations consistently exert the greatest influence on trajectory prediction, while neighboring interaction effects gradually diminish with increasing spatial separation. Furthermore, practical real-world deployment feasibility is investigated on the NVIDIA Jetson Xavier NX platform using edge-aware optimization strategies, including mixed-precision inference and graph-complexity reduction techniques for efficient resource-constrained inference, achieving 125.74 ms latency at 12.86 W. Additional edge deployment comparisons with HiVT and SIMPL approaches under identical hardware conditions demonstrate that the proposed framework provides a more favorable balance between computational efficiency and embedded deployment performance. Experimental evaluation on the Argoverse 1 dataset demonstrates a minimum Average Displacement Error (minADE) of 0.90 m, a minimum Final Displacement Error (minFDE) of 1.50 m, a Miss Rate (MR) of 0.19, and DAC = 0.95, while establishing an accuracy\u2013deployability operating point under embedded hardware constraints with low power consumption and practical inference throughput.<\/jats:p>","DOI":"10.3390\/s26154692","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:13:17Z","timestamp":1784826797000},"page":"4692","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Interpretable and Edge Deployable Spatio-Temporal Trajectory Prediction for Autonomous Driving"],"prefix":"10.3390","volume":"26","author":[{"given":"Rajesh Kannan","family":"Megalingam","sequence":"first","affiliation":[{"name":"Sustainable Mobility and Automotive Research Technology Centre (SMART), Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, Kerala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0815-3194","authenticated-orcid":false,"given":"Naveen Prasaad","family":"Selvarajan","sequence":"additional","affiliation":[{"name":"Sustainable Mobility and Automotive Research Technology Centre (SMART), Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, Kerala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pritty","family":"Vijay","sequence":"additional","affiliation":[{"name":"Sustainable Mobility and Automotive Research Technology Centre (SMART), Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, Kerala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A survey on motion prediction and risk assessment for intelligent vehicles","volume":"1","author":"Vasquez","year":"2014","journal-title":"ROBOMECH J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gindele, T., Brechtel, S., and Dillmann, R. (2010). A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, IEEE.","DOI":"10.1109\/ITSC.2010.5625262"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, B., Kang, C.M., Kim, J., Lee, S.H., Chung, C.C., and Choi, J.W. (2017). Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE.","DOI":"10.1109\/ITSC.2017.8317943"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (2016). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., and Chandraker, M. (2017). Desire: Distant future prediction in dynamic scenes with interacting agents. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.233"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jain, A., Zamir, A.R., Savarese, S., and Saxena, A. (2016). Structural-rnn: Deep learning on spatio-temporal graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., and Alahi, A. (2018). Social gan: Socially acceptable trajectories with generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., and Savarese, S. (2019). Sophie: An attentive gan for predicting paths compliant to social and physical constraints. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., and Schmid, C. (2020). Vectornet: Encoding hd maps and agent dynamics from vectorized representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liang, M., Yang, B., Hu, R., Chen, Y., Liao, R., Feng, S., and Urtasun, R. (2020). Learning lane graph representations for motion forecasting. Proceedings of the European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-58536-5_32"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gu, J., Sun, C., and Zhao, H. (2021). Densetnt: End-to-end trajectory prediction from dense goal sets. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Ye, L., Wang, J., Wu, K., and Lu, K. (2022). Hivt: Hierarchical vector transformer for multi-agent motion prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52688.2022.00862"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Weng, X., Ou, Y., and Kitani, K.M. (2021). Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00967"},{"key":"ref_14","unstructured":"Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.T.L., Ling, J., Roelofs, R., Bewley, A., Liu, C., and Venugopal, A. (2021). Scene transformer: A unified architecture for predicting multiple agent trajectories. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6531","DOI":"10.52202\/068431-0473","article-title":"Motion transformer with global intention localization and local movement refinement","volume":"35","author":"Shi","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nayakanti, N., Al-Rfou, R., Zhou, A., Goel, K., Refaat, K.S., and Sapp, B. (2022). Wayformer: Motion forecasting via simple & efficient attention networks. arXiv.","DOI":"10.1109\/ICRA48891.2023.10160609"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deo, N., and Trivedi, M.M. (2018). Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE.","DOI":"10.1109\/IVS.2018.8500493"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Deo, N., and Trivedi, M.M. (2018). Convolutional social pooling for vehicle trajectory prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE.","DOI":"10.1109\/CVPRW.2018.00196"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17654","DOI":"10.1109\/TITS.2022.3155749","article-title":"Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving","volume":"23","author":"Sheng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mo, X., Xing, Y., and Lv, C. (2021). Graph and recurrent neural network-based vehicle trajectory prediction for highway driving. arXiv.","DOI":"10.1109\/ITSC48978.2021.9564929"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abdelraouf, A., Gupta, R., and Han, K. (2023). Interaction-aware personalized vehicle trajectory prediction using temporal graph neural networks. arXiv.","DOI":"10.1109\/ITSC57777.2023.10421928"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gao, X., Jia, X., Li, Y., and Xiong, H. (2023). Dynamic scenario representation learning for motion forecasting with heterogeneous graph convolutional recurrent networks. arXiv.","DOI":"10.1109\/LRA.2023.3262150"},{"key":"ref_23","first-page":"203","article-title":"Multimodal trajectory prediction conditioned on lane-graph traversals","volume":"164","author":"Deo","year":"2022","journal-title":"Proc. Conf. Robot Learn. (CoRL)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, J., Guo, C., Guo, M., and Chen, J. (2021). Jointly learning agent and lane information for multimodal trajectory prediction. arXiv.","DOI":"10.1109\/ICPR56361.2022.9956525"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, S., Choi, J., Jeon, H., and Kum, D. (2022). Diverse multiple trajectory prediction using a two-stage prediction network trained with lane loss. arXiv.","DOI":"10.1109\/LRA.2022.3231525"},{"key":"ref_26","unstructured":"Luo, Y., Chen, L., and Hu, Z. (2021). Multi-modal motion prediction with transformer-based neural network for autonomous driving. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1109\/LRA.2024.3370039","article-title":"Simpl: A simple and efficient multi-agent motion prediction baseline for autonomous driving","volume":"9","author":"Zhang","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, Y., Cheng, Y., and Wang, K. (2025). Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.01126"},{"key":"ref_29","unstructured":"Maag, M., Gerber, P., and L\u00fcttgens, D. (2022). Explainable online lane change predictions on a digital twin with a layer normalized LSTM and layer-wise relevance propagation. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Peng, M., Guo, X., Chen, X., Zhu, M., and Chen, K. (2024). LC-LLM: Explainable lane-change intention and trajectory predictions with large language models. arXiv.","DOI":"10.1016\/j.commtr.2025.100170"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, P., Liu, H., Liu, X., Li, Y., Chen, J., He, Y., and Ma, J. (2024). Traj-Explainer: An explainable and robust multi-modal trajectory prediction approach. arXiv.","DOI":"10.1109\/ICRA55743.2025.11128379"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104358","DOI":"10.1016\/j.trc.2023.104358","article-title":"Why did the AI make that decision? Towards an explainable artificial intelligence for autonomous driving systems","volume":"156","author":"Atakishiyev","year":"2023","journal-title":"Transp. Res. Part C"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, Z., Mo, X., and Lv, C. (2022). ReCoAt: A deep learning-based framework for multi-modal motion prediction in autonomous driving. arXiv.","DOI":"10.1109\/ITSC55140.2022.9922325"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, J., Qiao, S., Li, H., Sun, B., Gao, F., Hu, H., and Zhao, R. (2024). Goal-guided graph attention network with interactive state refinement for multi-agent trajectory prediction. Sensors, 24.","DOI":"10.3390\/s24072065"},{"key":"ref_35","first-page":"906","article-title":"Benchmarking deep learning models on NVIDIA Jetson for real-time systems: An empirical investigation","volume":"260","author":"Swaminathan","year":"2024","journal-title":"Embed. Syst. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cordova-Cardenas, R., Amor, D., and Guti\u00e9rrez, A. (2025). Edge AI: A survey and deployment framework for neural networks on embedded systems. Electronics, 14.","DOI":"10.3390\/electronics14244877"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guo, C., Fan, S., Chen, C., Zhao, W., Wang, J., Zhang, Y., and Chen, Y. (2024). QINET: Query-informed multi-agent motion prediction for autonomous driving. Sensors, 24.","DOI":"10.3390\/s24010009"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8114","DOI":"10.1109\/TWC.2025.3564356","article-title":"Task-Driven Priority-Aware Computation Offloading Using Deep Reinforcement Learning","volume":"24","author":"Hao","year":"2025","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Fang, L., Jiang, Q., and Zhou, B. (2021). Multimodal motion prediction with stacked transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Aydemir, G., Akan, A.K., and G\u00fcney, F. (2023). Adapt: Efficient multi-agent trajectory prediction with adaptation. Proceedings of the IEEE\/CVF International Conference on Computer VisiFigon, IEEE.","DOI":"10.1109\/ICCV51070.2023.00762"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4692\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:17:55Z","timestamp":1784827075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4692"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,23]]},"references-count":40,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26154692"],"URL":"https:\/\/doi.org\/10.3390\/s26154692","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,23]]}}}