{"id":"https://openalex.org/W7130411373","doi":"https://doi.org/10.1109/tencon66050.2025.11375082","title":"TASNet: A Temporal Attention Network for Front-Wheel Axis Angle Prediction in Real-World Driving","display_name":"TASNet: A Temporal Attention Network for Front-Wheel Axis Angle Prediction in Real-World Driving","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7130411373","doi":"https://doi.org/10.1109/tencon66050.2025.11375082"},"language":null,"primary_location":{"id":"doi:10.1109/tencon66050.2025.11375082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047449114","display_name":"Pritam Chakraborty","orcid":null},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pritam Chakraborty","raw_affiliation_strings":["School of Computer Engineering, KIIT University,Bhubaneswar,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, KIIT University,Bhubaneswar,India","institution_ids":["https://openalex.org/I67357951"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006426949","display_name":"Anjan Bandyopadhyay","orcid":"https://orcid.org/0000-0001-7670-2269"},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anjan Bandyopadhyay","raw_affiliation_strings":["School of Computer Engineering, KIIT University,Bhubaneswar,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, KIIT University,Bhubaneswar,India","institution_ids":["https://openalex.org/I67357951"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67357951"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69709942,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"165","last_page":"169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.7523999810218811,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.7523999810218811,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.07249999791383743,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.06499999761581421,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7728999853134155},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5931000113487244},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5874999761581421},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.5353000164031982},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5332000255584717},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5004000067710876},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4975999891757965},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45500001311302185}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7728999853134155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7110000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6360999941825867},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.5353000164031982},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5332000255584717},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4975999891757965},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4129999876022339},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38429999351501465},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34869998693466187},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon66050.2025.11375082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.792720377445221}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325255","display_name":"Ministry of Electronics and Information technology","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2886934227","https://openalex.org/W2901870313","https://openalex.org/W2987039128","https://openalex.org/W3011785450","https://openalex.org/W3034552520","https://openalex.org/W3201271601","https://openalex.org/W4285042965","https://openalex.org/W4292347911","https://openalex.org/W4312127538","https://openalex.org/W4389666829","https://openalex.org/W4409590115"],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"navigation":[1],"in":[2,31,97],"unstructured":[3],"environments":[4],"such":[5,32],"as":[6],"off-road":[7],"terrain":[8],"remains":[9],"a":[10,37,44,92,148],"significant":[11],"challenge":[12],"due":[13],"to":[14,56,76,87,151],"dynamic":[15],"obstacles":[16],"and":[17,53,90,144,157],"unpredictable":[18],"surfaces.":[19],"Most":[20],"vision-based":[21],"models":[22],"depend":[23],"on":[24,101,120,132],"structured":[25],"road":[26],"features,":[27],"limiting":[28],"their":[29],"applicability":[30],"settings.":[33],"We":[34],"propose":[35],"TASNet,":[36],"vision-only":[38],"deep":[39],"learning":[40],"architecture":[41],"that":[42],"integrates":[43],"lightweight":[45],"CNN":[46],"backbone,":[47],"bidirectional":[48],"LSTM":[49],"for":[50,118,154],"temporal":[51,142],"context,":[52],"spatial":[54],"attention":[55],"prioritize":[57],"obstacle-relevant":[58],"features.":[59],"The":[60],"model":[61],"is":[62,116],"trained":[63],"using":[64],"CARLA-generated":[65],"synthetic":[66],"data":[67],"with":[68,129],"domain":[69],"randomization":[70],"(e.g.,":[71],"lighting,":[72],"weather),":[73],"improving":[74],"generalization":[75],"real-world":[77],"variability.":[78],"TASNet":[79,115,146],"reduces":[80],"steering":[81],"angle":[82],"error":[83],"by":[84],"27.4%":[85],"compared":[86],"NVIDIA":[88,133],"PilotNet":[89],"achieves":[91],"92%":[93],"collision-free":[94],"success":[95],"rate":[96],"simulation.":[98],"Real-world":[99],"tests":[100],"an":[102],"all-terrain":[103],"vehicle":[104],"(ATV)":[105],"dataset":[106],"confirm":[107],"its":[108],"robustness":[109],"across":[110],"varied":[111],"outdoor":[112],"scenarios.":[113],"Moreover,":[114],"optimized":[117],"deployment":[119],"resource-constrained":[121],"hardware,":[122],"delivering":[123],"real-time":[124],"inference":[125],"at":[126],"45":[127],"FPS":[128],"8.2W":[130],"power":[131],"Jetson":[134],"AGX.":[135],"Ablation":[136],"studies":[137],"underscore":[138],"the":[139],"importance":[140],"of":[141],"modeling":[143],"attention.":[145],"offers":[147],"cost-effective":[149],"alternative":[150],"LiDAR-based":[152],"systems":[153],"field":[155],"robotics":[156],"disaster":[158],"response.":[159]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-19T00:00:00"}
