{"id":"https://openalex.org/W7162690354","doi":"https://doi.org/10.48550/arxiv.2605.27418","title":"Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections","display_name":"Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7162690354","doi":"https://doi.org/10.48550/arxiv.2605.27418"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27418","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27418","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27418","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135211448","display_name":"Wenzhe Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Wenzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137222830","display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0003-1719-0034"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.6133000254631042,"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":0.6133000254631042,"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/T10524","display_name":"Traffic control and management","score":0.15639999508857727,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.08169999718666077,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/differentiable-function","display_name":"Differentiable function","score":0.6189000010490417},{"id":"https://openalex.org/keywords/futures-studies","display_name":"Futures studies","score":0.5900999903678894},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.49720001220703125},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4803999960422516},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.47049999237060547},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.43380001187324524},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.39419999718666077},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.3828999996185303},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.36649999022483826}],"concepts":[{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.6189000010490417},{"id":"https://openalex.org/C64848388","wikidata":"https://www.wikidata.org/wiki/Q188867","display_name":"Futures studies","level":2,"score":0.5900999903678894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5861999988555908},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.49720001220703125},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.3828999996185303},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.36649999022483826},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3555999994277954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C132835097","wikidata":"https://www.wikidata.org/wiki/Q7663745","display_name":"System safety","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C94279714","wikidata":"https://www.wikidata.org/wiki/Q6496962","display_name":"Safety engineering","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25839999318122864},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27418","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27418","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27418","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27418","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4325169026851654,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"imminent":[1],"integration":[2],"of":[3,28,52,86,105],"autonomous":[4],"vehicles":[5],"and":[6,117,148],"mobile":[7],"robots":[8],"in":[9,136],"urban":[10],"settings":[11],"presents":[12],"a":[13,40,56,65,115,124],"critical":[14],"safety":[15,46,80,107,120,144],"challenge":[16],"for":[17],"future":[18,71,106],"intelligent":[19],"transportation":[20],"systems.":[21],"This":[22],"paper":[23],"addresses":[24],"the":[25,50,84,94,103],"complex":[26],"problem":[27],"coordinating":[29],"heterogeneous":[30],"agents":[31,63,99],"with":[32,108],"disparate":[33],"dynamics":[34,67],"at":[35],"unregulated":[36],"intersections.":[37],"We":[38],"introduce":[39],"novel":[41],"framework,":[42],"differentiable":[43,79],"model":[44,68],"predictive":[45,97],"(DMPS),":[47],"which":[48],"embeds":[49],"foresight":[51],"model-predictive":[53],"control":[54],"into":[55,123],"data-driven,":[57],"end-to-end":[58],"reinforcement":[59],"learning":[60],"architecture.":[61],"DMPS":[62,128],"learn":[64],"latent":[66],"to":[69,110,132],"predict":[70],"trajectories":[72],"contingent":[73],"on":[74],"their":[75,111],"actions.":[76],"A":[77],"learned,":[78],"critic":[81],"then":[82],"evaluates":[83],"risk":[85],"these":[87],"trajectories.":[88],"Crucially,":[89],"by":[90],"leveraging":[91],"backpropagation":[92],"through":[93],"entire":[95],"unrolled":[96],"model,":[98],"can":[100],"efficiently":[101],"compute":[102],"gradient":[104],"respect":[109],"current":[112],"action,":[113],"enabling":[114],"minimal":[116],"precise":[118],"online":[119],"correction.":[121],"Integrated":[122],"multi-agent":[125],"training":[126],"scheme,":[127],"virtually":[129],"eliminates":[130],"collisions":[131],"less":[133],"than":[134],"5.6%":[135],"high-density,":[137],"mixed":[138],"vehicle-robot":[139],"traffic":[140,149],"simulations,":[141],"demonstrating":[142],"state-of-the-art":[143],"without":[145],"compromising":[146],"energy":[147],"efficiency.":[150]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}
