{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/traf-align-trajectory-aware-feature-alignment","title":"TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception","arxiv_id":"2503.19391","date":"2025-03-25","proceeding":"CVPR 2025 1","authors":["Zhiying Song","Lei Yang","Fuxi Wen","Jun Li"],"abstract":"Cooperative perception presents significant potential for enhancing the sensing capabilities of individual vehicles, however, inter-agent latency remains a critical challenge. Latencies cause misalignments in both spatial and semantic features, complicating the fusion of real-time observations from the ego vehicle with delayed data from others. To address these issues, we propose TraF-Align, a novel framework that learns the flow path of features by predicting the feature-level trajectory of objects from past observations up to the ego vehicle's current time. By generating temporally ordered sampling points along these paths, TraF-Align directs attention from the current-time query to relevant historical features along each trajectory, supporting the reconstruction of current-time features and promoting semantic interaction across multiple frames. This approach corrects spatial misalignment and ensures semantic consistency across agents, effectively compensating for motion and achieving coherent feature fusion. Experiments on two real-world datasets, V2V4Real and DAIR-V2X-Seq, show that TraF-Align sets a new benchmark for asynchronous cooperative perception.","url_abs":"https://arxiv.org/abs/2503.19391v1","url_pdf":"https://arxiv.org/pdf/2503.19391v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"traf-align-trajectory-aware-feature-alignment","repo_url":"https://github.com/zhyings/traf-align","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2503.19391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.19391"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zhyings/traf-align","reach":{"status":"ok"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/zhyingS/TraF-Align","reach":{"status":"ok"}}],"summary":{"ran_fixture":1,"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"99113e547d82cc5b","entry":"map_gt_cls","repo":"zhyings/traf-align","repo_kind":"official","path":"tools/inference.py","file_url":"https://github.com/zhyings/traf-align/blob/HEAD/tools/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"99113e547d82cc5b"}},{"code_sha256_prefix":"cf7610d966af174a","entry":"mask_box_out_of_range","repo":"zhyings/traf-align","repo_kind":"official","path":"tools/inference.py","file_url":"https://github.com/zhyings/traf-align/blob/HEAD/tools/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cf7610d966af174a"}},{"code_sha256_prefix":"0741a53033a62c43","entry":"mask_ego_box","repo":"zhyings/traf-align","repo_kind":"official","path":"tools/inference.py","file_url":"https://github.com/zhyings/traf-align/blob/HEAD/tools/inference.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0741a53033a62c43"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}