{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/autonomous-driving/papers/7","list_of":"/task/autonomous-driving","task":"Autonomous Driving","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":7,"pages_in_order":61,"rows_per_page":100,"rows":[601,700],"of":6092,"counts":{"archive_papers_tagged":6092,"with_a_code_link":2091,"where_syntology_ran_a_sample":470,"not_listed_spam_title":0,"listed":6092,"listed_where_code_ran":470,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":415,"every_run_a_failure_of_syntologys_instrument":55,"listed_with_a_run_with_no_instrument_failure":415,"listed_every_run_a_failure_of_syntologys_instrument":55,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/autonomous-driving","prev":"/task/autonomous-driving/papers/6","next":"/task/autonomous-driving/papers/8","papers":[{"url":"/paper/diffusiondrive-truncated-diffusion-model-for","slug":"diffusiondrive-truncated-diffusion-model-for","title":"DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving","date":"2024-11-22","arxiv_id":"2411.15139","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusiondrive-truncated-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2411.15139","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.15139"}},"official":{"repos":["hustvl/diffusiondrive"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neuro-symbolic-evaluation-of-text-to-video","slug":"neuro-symbolic-evaluation-of-text-to-video","title":"Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification","date":"2024-11-22","arxiv_id":"2411.16718","repositories_listed":1,"syntology":null},{"url":"/paper/warlearn-weather-adaptive-representation","slug":"warlearn-weather-adaptive-representation","title":"WARLearn: Weather-Adaptive Representation Learning","date":"2024-11-21","arxiv_id":"2411.14095","repositories_listed":1,"syntology":null},{"url":"/paper/a-resource-efficient-fusion-network-for","slug":"a-resource-efficient-fusion-network-for","title":"A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data","date":"2024-11-20","arxiv_id":"2411.13311","repositories_listed":1,"syntology":null},{"url":"/paper/drivemllm-a-benchmark-for-spatial","slug":"drivemllm-a-benchmark-for-spatial","title":"DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous Driving","date":"2024-11-20","arxiv_id":"2411.13112","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/drivemllm-a-benchmark-for-spatial#ran","syntology_url":"https://syntology.ai/paper/2411.13112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.13112"}},"official":{"repos":["xiandaguo/drive-mllm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/whales-a-multi-agent-scheduling-dataset-for","slug":"whales-a-multi-agent-scheduling-dataset-for","title":"WHALES: A Multi-agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving","date":"2024-11-20","arxiv_id":"2411.13340","repositories_listed":1,"syntology":null},{"url":"/paper/ycb-luma-ycb-object-dataset-with-luminance","slug":"ycb-luma-ycb-object-dataset-with-luminance","title":"YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization","date":"2024-11-20","arxiv_id":"2411.13149","repositories_listed":1,"syntology":null},{"url":"/paper/gaussianpretrain-a-simple-unified-3d-gaussian","slug":"gaussianpretrain-a-simple-unified-3d-gaussian","title":"GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous Driving","date":"2024-11-19","arxiv_id":"2411.12452","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gaussianpretrain-a-simple-unified-3d-gaussian#ran","syntology_url":"https://syntology.ai/paper/2411.12452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.12452"}},"official":{"repos":["public-bots/gaussianpretrain"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/m3d-dual-stream-selective-state-spaces-and","slug":"m3d-dual-stream-selective-state-spaces-and","title":"M3D: Dual-Stream Selective State Spaces and Depth-Driven Framework for High-Fidelity Single-View 3D Reconstruction","date":"2024-11-19","arxiv_id":"2411.12635","repositories_listed":1,"syntology":null},{"url":"/paper/motif-channel-opened-in-a-white-box-stereo","slug":"motif-channel-opened-in-a-white-box-stereo","title":"Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph","date":"2024-11-19","arxiv_id":"2411.12426","repositories_listed":1,"syntology":null},{"url":"/paper/robust-3d-semantic-occupancy-prediction-with","slug":"robust-3d-semantic-occupancy-prediction-with","title":"Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation","date":"2024-11-19","arxiv_id":"2411.12177","repositories_listed":1,"syntology":null},{"url":"/paper/desire-gs-4d-street-gaussians-for-static","slug":"desire-gs-4d-street-gaussians-for-static","title":"DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes","date":"2024-11-18","arxiv_id":"2411.11921","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/desire-gs-4d-street-gaussians-for-static#ran","syntology_url":"https://syntology.ai/paper/2411.11921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.11921"}},"official":{"repos":["chengweialan/desire-gs"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/drivingsphere-building-a-high-fidelity-4d","slug":"drivingsphere-building-a-high-fidelity-4d","title":"DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation","date":"2024-11-18","arxiv_id":"2411.11252","repositories_listed":1,"syntology":null},{"url":"/paper/alocc-adaptive-lifting-based-3d-semantic","slug":"alocc-adaptive-lifting-based-3d-semantic","title":"ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction","date":"2024-11-12","arxiv_id":"2411.07725","repositories_listed":1,"syntology":null},{"url":"/paper/owled-outlier-weighed-layerwise-pruning-for","slug":"owled-outlier-weighed-layerwise-pruning-for","title":"OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework","date":"2024-11-12","arxiv_id":"2411.07711","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-moral-machine-experiment-on-large","slug":"large-scale-moral-machine-experiment-on-large","title":"Large-scale moral machine experiment on large language models","date":"2024-11-11","arxiv_id":"2411.06790","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/large-scale-moral-machine-experiment-on-large#ran","syntology_url":"https://syntology.ai/paper/2411.06790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.06790"}},"official":{"repos":["kztakemoto/mmllm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lssinst-improving-geometric-modeling-in-lss","slug":"lssinst-improving-geometric-modeling-in-lss","title":"LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation","date":"2024-11-09","arxiv_id":"2411.06173","repositories_listed":1,"syntology":null},{"url":"/paper/igdrivsim-a-benchmark-for-the-imitation-gap","slug":"igdrivsim-a-benchmark-for-the-imitation-gap","title":"IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving","date":"2024-11-07","arxiv_id":"2411.04653","repositories_listed":1,"syntology":null},{"url":"/paper/learning-multiple-initial-solutions-to","slug":"learning-multiple-initial-solutions-to","title":"Learning Multiple Initial Solutions to Optimization Problems","date":"2024-11-04","arxiv_id":"2411.02158","repositories_listed":1,"syntology":null},{"url":"/paper/polar-r-cnn-end-to-end-lane-detection-with","slug":"polar-r-cnn-end-to-end-lane-detection-with","title":"Polar R-CNN: End-to-End Lane Detection with Fewer Anchors","date":"2024-11-03","arxiv_id":"2411.01499","repositories_listed":1,"syntology":null},{"url":"/paper/road-waymo-action-awareness-at-scale-for","slug":"road-waymo-action-awareness-at-scale-for","title":"ROAD-Waymo: Action Awareness at Scale for Autonomous Driving","date":"2024-11-03","arxiv_id":"2411.01683","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":10,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/road-waymo-action-awareness-at-scale-for#ran","syntology_url":"https://syntology.ai/paper/2411.01683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01683"}},"official":{"repos":["salmank255/ROAD_Waymo_Baseline"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hoptrack-a-real-time-multi-object-tracking","slug":"hoptrack-a-real-time-multi-object-tracking","title":"HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices","date":"2024-11-01","arxiv_id":"2411.00608","repositories_listed":1,"syntology":null},{"url":"/paper/ra-pbrl-provably-efficient-risk-aware","slug":"ra-pbrl-provably-efficient-risk-aware","title":"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning","date":"2024-10-31","arxiv_id":"2410.23569","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-approach-to-generate-safe","slug":"an-efficient-approach-to-generate-safe","title":"An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion","date":"2024-10-29","arxiv_id":"2410.22314","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspectral-imaging-based-perception-in","slug":"hyperspectral-imaging-based-perception-in","title":"Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models","date":"2024-10-29","arxiv_id":"2410.22101","repositories_listed":1,"syntology":null},{"url":"/paper/senna-bridging-large-vision-language-models","slug":"senna-bridging-large-vision-language-models","title":"Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving","date":"2024-10-29","arxiv_id":"2410.22313","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/senna-bridging-large-vision-language-models#ran","syntology_url":"https://syntology.ai/paper/2410.22313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.22313"}},"official":{"repos":["hustvl/senna"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-black-box-explainability-of-object","slug":"on-the-black-box-explainability-of-object","title":"On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications","date":"2024-10-28","arxiv_id":"2411.00818","repositories_listed":1,"syntology":null},{"url":"/paper/cloudspam-contrastive-learning-on-unlabeled","slug":"cloudspam-contrastive-learning-on-unlabeled","title":"CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo","date":"2024-10-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-fields-in-robotics-a-survey","slug":"neural-fields-in-robotics-a-survey","title":"Neural Fields in Robotics: A Survey","date":"2024-10-26","arxiv_id":"2410.20220","repositories_listed":1,"syntology":null},{"url":"/paper/carla2real-a-tool-for-reducing-the-sim2real","slug":"carla2real-a-tool-for-reducing-the-sim2real","title":"CARLA2Real: a tool for reducing the sim2real gap in CARLA simulator","date":"2024-10-23","arxiv_id":"2410.18238","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-vehicle-to-vehicle-communication","slug":"real-time-vehicle-to-vehicle-communication","title":"Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads","date":"2024-10-23","arxiv_id":"2410.17576","repositories_listed":1,"syntology":null},{"url":"/paper/pedestrian-motion-prediction-evaluation-for","slug":"pedestrian-motion-prediction-evaluation-for","title":"Pedestrian motion prediction evaluation for urban autonomous driving","date":"2024-10-22","arxiv_id":"2410.16864","repositories_listed":1,"syntology":null},{"url":"/paper/spikmamba-when-snn-meets-mamba-in-event-based","slug":"spikmamba-when-snn-meets-mamba-in-event-based","title":"SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition","date":"2024-10-22","arxiv_id":"2410.16746","repositories_listed":1,"syntology":null},{"url":"/paper/bench4merge-a-comprehensive-benchmark-for","slug":"bench4merge-a-comprehensive-benchmark-for","title":"Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive Vehicles","date":"2024-10-21","arxiv_id":"2410.15912","repositories_listed":1,"syntology":null},{"url":"/paper/generalizing-motion-planners-with-mixture-of","slug":"generalizing-motion-planners-with-mixture-of","title":"Generalizing Motion Planners with Mixture of Experts for Autonomous Driving","date":"2024-10-21","arxiv_id":"2410.15774","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generalizing-motion-planners-with-mixture-of#ran","syntology_url":"https://syntology.ai/paper/2410.15774","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15774"}},"official":{"repos":["tsinghua-mars-lab/statetransformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mini-internvl-a-flexible-transfer-pocket","slug":"mini-internvl-a-flexible-transfer-pocket","title":"Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance","date":"2024-10-21","arxiv_id":"2410.16261","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":2,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mini-internvl-a-flexible-transfer-pocket#ran","syntology_url":"https://syntology.ai/paper/2410.16261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.16261"}},"official":{"repos":["opengvlab/internvl"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/xai-based-feature-ensemble-for-enhanced","slug":"xai-based-feature-ensemble-for-enhanced","title":"XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems","date":"2024-10-20","arxiv_id":"2410.15405","repositories_listed":1,"syntology":null},{"url":"/paper/3d-multi-object-tracking-employing-ms-glmb","slug":"3d-multi-object-tracking-employing-ms-glmb","title":"3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving","date":"2024-10-19","arxiv_id":"2410.14977","repositories_listed":1,"syntology":null},{"url":"/paper/part-whole-relational-fusion-towards-multi","slug":"part-whole-relational-fusion-towards-multi","title":"Part-Whole Relational Fusion Towards Multi-Modal Scene Understanding","date":"2024-10-19","arxiv_id":"2410.14944","repositories_listed":1,"syntology":null},{"url":"/paper/unidrive-towards-universal-driving-perception","slug":"unidrive-towards-universal-driving-perception","title":"UniDrive: Towards Universal Driving Perception Across Camera Configurations","date":"2024-10-17","arxiv_id":"2410.13864","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unidrive-towards-universal-driving-perception#ran","syntology_url":"https://syntology.ai/paper/2410.13864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13864"}},"official":{"repos":["ywyeli/unidrive"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/real-time-stereo-based-3d-object-detection","slug":"real-time-stereo-based-3d-object-detection","title":"Real-time Stereo-based 3D Object Detection for Streaming Perception","date":"2024-10-16","arxiv_id":"2410.12394","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-prototype-network-for-explainable","slug":"sparse-prototype-network-for-explainable","title":"Sparse Prototype Network for Explainable Pedestrian Behavior Prediction","date":"2024-10-16","arxiv_id":"2410.12195","repositories_listed":1,"syntology":null},{"url":"/paper/teocc-radar-camera-multi-modal-occupancy","slug":"teocc-radar-camera-multi-modal-occupancy","title":"TEOcc: Radar-camera Multi-modal Occupancy Prediction via Temporal Enhancement","date":"2024-10-15","arxiv_id":"2410.11228","repositories_listed":1,"syntology":null},{"url":"/paper/weatherdg-llm-assisted-procedural-weather","slug":"weatherdg-llm-assisted-procedural-weather","title":"WeatherDG: LLM-assisted Diffusion Model for Procedural Weather Generation in Domain-Generalized Semantic Segmentation","date":"2024-10-15","arxiv_id":"2410.12075","repositories_listed":1,"syntology":null},{"url":"/paper/condition-aware-multimodal-fusion-for-robust","slug":"condition-aware-multimodal-fusion-for-robust","title":"CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes","date":"2024-10-14","arxiv_id":"2410.10791","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/condition-aware-multimodal-fusion-for-robust#ran","syntology_url":"https://syntology.ai/paper/2410.10791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10791"}},"official":{"repos":["timbroed/cafuser"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploiting-local-features-and-range-images","slug":"exploiting-local-features-and-range-images","title":"Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic Segmentation","date":"2024-10-14","arxiv_id":"2410.10510","repositories_listed":1,"syntology":null},{"url":"/paper/rnn-based-linear-parameter-varying-adaptive","slug":"rnn-based-linear-parameter-varying-adaptive","title":"RNN-based linear parameter varying adaptive model predictive control for autonomous driving","date":"2024-10-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/study-on-the-helpfulness-of-explainable","slug":"study-on-the-helpfulness-of-explainable","title":"Study on the Helpfulness of Explainable Artificial Intelligence","date":"2024-10-14","arxiv_id":"2410.11896","repositories_listed":1,"syntology":null},{"url":"/paper/towards-calibrated-losses-for-adversarial","slug":"towards-calibrated-losses-for-adversarial","title":"Towards Calibrated Losses for Adversarial Robust Reject Option Classification","date":"2024-10-14","arxiv_id":"2410.10736","repositories_listed":1,"syntology":null},{"url":"/paper/loli-street-benchmarking-low-light-image","slug":"loli-street-benchmarking-low-light-image","title":"LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond","date":"2024-10-13","arxiv_id":"2410.09831","repositories_listed":1,"syntology":null},{"url":"/paper/lord-adapting-differentiable-driving-policies","slug":"lord-adapting-differentiable-driving-policies","title":"LoRD: Adapting Differentiable Driving Policies to Distribution Shifts","date":"2024-10-13","arxiv_id":"2410.09681","repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-surface-reflections-in-maritime","slug":"impact-of-surface-reflections-in-maritime","title":"Impact of Surface Reflections in Maritime Obstacle Detection","date":"2024-10-11","arxiv_id":"2410.08713","repositories_listed":1,"syntology":null},{"url":"/paper/bevloc-cross-view-localization-and-matching","slug":"bevloc-cross-view-localization-and-matching","title":"BEVLoc: Cross-View Localization and Matching via Birds-Eye-View Synthesis","date":"2024-10-08","arxiv_id":"2410.06410","repositories_listed":1,"syntology":null},{"url":"/paper/demo-decoupling-motion-forecasting-into","slug":"demo-decoupling-motion-forecasting-into","title":"DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States","date":"2024-10-08","arxiv_id":"2410.05982","repositories_listed":1,"syntology":null},{"url":"/paper/prfusion-toward-effective-and-robust-multi","slug":"prfusion-toward-effective-and-robust-multi","title":"PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion","date":"2024-10-07","arxiv_id":"2410.04939","repositories_listed":1,"syntology":null},{"url":"/paper/make-interval-bound-propagation-great-again","slug":"make-interval-bound-propagation-great-again","title":"Make Interval Bound Propagation great again","date":"2024-10-04","arxiv_id":"2410.03373","repositories_listed":1,"syntology":null},{"url":"/paper/model-developmental-safety-a-safety-centric","slug":"model-developmental-safety-a-safety-centric","title":"A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety","date":"2024-10-04","arxiv_id":"2410.03955","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/model-developmental-safety-a-safety-centric#ran","syntology_url":"https://syntology.ai/paper/2410.03955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03955"}},"official":{"repos":["ganglii/devsafety"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/stone-a-submodular-optimization-framework-for","slug":"stone-a-submodular-optimization-framework-for","title":"STONE: A Submodular Optimization Framework for Active 3D Object Detection","date":"2024-10-04","arxiv_id":"2410.03918","repositories_listed":1,"syntology":null},{"url":"/paper/abstract-reward-processes-leveraging-state","slug":"abstract-reward-processes-leveraging-state","title":"Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation","date":"2024-10-03","arxiv_id":"2410.02172","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/abstract-reward-processes-leveraging-state#ran","syntology_url":"https://syntology.ai/paper/2410.02172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02172"}},"official":{"repos":["shreyasc-13/star"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/agent-security-bench-asb-formalizing-and","slug":"agent-security-bench-asb-formalizing-and","title":"Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents","date":"2024-10-03","arxiv_id":"2410.02644","repositories_listed":1,"syntology":{"n":13,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/agent-security-bench-asb-formalizing-and#ran","syntology_url":"https://syntology.ai/paper/2410.02644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02644"}},"official":{"repos":["agiresearch/asb"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/spatial-temporal-multi-cuts-for-online","slug":"spatial-temporal-multi-cuts-for-online","title":"Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking","date":"2024-10-03","arxiv_id":"2410.02638","repositories_listed":1,"syntology":null},{"url":"/paper/open3dtrack-towards-open-vocabulary-3d-multi","slug":"open3dtrack-towards-open-vocabulary-3d-multi","title":"Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking","date":"2024-10-02","arxiv_id":"2410.01678","repositories_listed":1,"syntology":null},{"url":"/paper/perceptual-piercing-human-visual-cue-based","slug":"perceptual-piercing-human-visual-cue-based","title":"Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions","date":"2024-10-02","arxiv_id":"2410.01225","repositories_listed":1,"syntology":null},{"url":"/paper/gspr-multimodal-place-recognition-using-3d","slug":"gspr-multimodal-place-recognition-using-3d","title":"GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving","date":"2024-10-01","arxiv_id":"2410.00299","repositories_listed":1,"syntology":null},{"url":"/paper/daocc-3d-object-detection-assisted-multi","slug":"daocc-3d-object-detection-assisted-multi","title":"DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction","date":"2024-09-30","arxiv_id":"2409.19972","repositories_listed":1,"syntology":null},{"url":"/paper/does-end-to-end-autonomous-driving-really","slug":"does-end-to-end-autonomous-driving-really","title":"Does End-to-End Autonomous Driving Really Need Perception Tasks?","date":"2024-09-26","arxiv_id":"2409.18341","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/does-end-to-end-autonomous-driving-really#ran","syntology_url":"https://syntology.ai/paper/2409.18341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18341"}},"official":{"repos":["peidongli/ssr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dualad-dual-layer-planning-for-reasoning-in","slug":"dualad-dual-layer-planning-for-reasoning-in","title":"DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving","date":"2024-09-26","arxiv_id":"2409.18053","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-motion-prediction-a-lightweight","slug":"efficient-motion-prediction-a-lightweight","title":"Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed","date":"2024-09-24","arxiv_id":"2409.16154","repositories_listed":1,"syntology":null},{"url":"/paper/learning-multiple-probabilistic-decisions","slug":"learning-multiple-probabilistic-decisions","title":"Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving","date":"2024-09-24","arxiv_id":"2409.15730","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-pedestrian-trajectory-prediction","slug":"enhancing-pedestrian-trajectory-prediction","title":"Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information","date":"2024-09-23","arxiv_id":"2409.15224","repositories_listed":1,"syntology":null},{"url":"/paper/mctrack-a-unified-3d-multi-object-tracking","slug":"mctrack-a-unified-3d-multi-object-tracking","title":"MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving","date":"2024-09-23","arxiv_id":"2409.16149","repositories_listed":1,"syntology":null},{"url":"/paper/margin-bounded-confidence-scores-for-out-of","slug":"margin-bounded-confidence-scores-for-out-of","title":"Margin-bounded Confidence Scores for Out-of-Distribution Detection","date":"2024-09-22","arxiv_id":"2410.07185","repositories_listed":1,"syntology":null},{"url":"/paper/onebev-using-one-panoramic-image-for-bird-s","slug":"onebev-using-one-panoramic-image-for-bird-s","title":"OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping","date":"2024-09-20","arxiv_id":"2409.13912","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-automatic-3d-annotation-of-traffic-1","slug":"accurate-automatic-3d-annotation-of-traffic-1","title":"Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving","date":"2024-09-19","arxiv_id":"2409.12620","repositories_listed":1,"syntology":null},{"url":"/paper/annealed-winner-takes-all-for-motion","slug":"annealed-winner-takes-all-for-motion","title":"Annealed Winner-Takes-All for Motion Forecasting","date":"2024-09-17","arxiv_id":"2409.11172","repositories_listed":1,"syntology":null},{"url":"/paper/hs3-bench-a-benchmark-and-strong-baseline-for","slug":"hs3-bench-a-benchmark-and-strong-baseline-for","title":"HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios","date":"2024-09-17","arxiv_id":"2409.11205","repositories_listed":1,"syntology":null},{"url":"/paper/ultimatedo-an-efficient-framework-to-marry","slug":"ultimatedo-an-efficient-framework-to-marry","title":"UltimateDO: An Efficient Framework to Marry Occupancy Prediction with 3D Object Detection via Channel2height","date":"2024-09-17","arxiv_id":"2409.11160","repositories_listed":1,"syntology":null},{"url":"/paper/comamba-real-time-cooperative-perception","slug":"comamba-real-time-cooperative-perception","title":"CoMamba: Real-time Cooperative Perception Unlocked with State Space Models","date":"2024-09-16","arxiv_id":"2409.10699","repositories_listed":1,"syntology":null},{"url":"/paper/scaleflow-robust-and-accurate-estimation-of","slug":"scaleflow-robust-and-accurate-estimation-of","title":"ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video","date":"2024-09-16","arxiv_id":"2409.12202","repositories_listed":1,"syntology":null},{"url":"/paper/seal-towards-safe-autonomous-driving-via","slug":"seal-towards-safe-autonomous-driving-via","title":"SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation","date":"2024-09-16","arxiv_id":"2409.10320","repositories_listed":1,"syntology":null},{"url":"/paper/difsd-ego-centric-fully-sparse-paradigm-with","slug":"difsd-ego-centric-fully-sparse-paradigm-with","title":"DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving","date":"2024-09-15","arxiv_id":"2409.09777","repositories_listed":1,"syntology":null},{"url":"/paper/mulcpred-learning-multi-modal-concepts-for","slug":"mulcpred-learning-multi-modal-concepts-for","title":"MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction","date":"2024-09-14","arxiv_id":"2409.09446","repositories_listed":1,"syntology":null},{"url":"/paper/opus-occupancy-prediction-using-a-sparse-set","slug":"opus-occupancy-prediction-using-a-sparse-set","title":"OPUS: Occupancy Prediction Using a Sparse Set","date":"2024-09-14","arxiv_id":"2409.09350","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/opus-occupancy-prediction-using-a-sparse-set#ran","syntology_url":"https://syntology.ai/paper/2409.09350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09350"}},"official":{"repos":["jbwang1997/OPUS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/genmapping-unleashing-the-potential-of","slug":"genmapping-unleashing-the-potential-of","title":"GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction","date":"2024-09-13","arxiv_id":"2409.08688","repositories_listed":1,"syntology":null},{"url":"/paper/led-light-enhanced-depth-estimation-at-night","slug":"led-light-enhanced-depth-estimation-at-night","title":"LED: Light Enhanced Depth Estimation at Night","date":"2024-09-12","arxiv_id":"2409.08031","repositories_listed":1,"syntology":null},{"url":"/paper/regents-real-world-safety-critical-driving","slug":"regents-real-world-safety-critical-driving","title":"ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable","date":"2024-09-12","arxiv_id":"2409.07830","repositories_listed":1,"syntology":null},{"url":"/paper/rocas-root-cause-analysis-of-autonomous","slug":"rocas-root-cause-analysis-of-autonomous","title":"ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation","date":"2024-09-12","arxiv_id":"2409.07774","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-inverse-constrained-reinforcement","slug":"a-survey-of-inverse-constrained-reinforcement","title":"A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges","date":"2024-09-11","arxiv_id":"2409.07569","repositories_listed":1,"syntology":null},{"url":"/paper/minidrive-more-efficient-vision-language","slug":"minidrive-more-efficient-vision-language","title":"MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous Driving","date":"2024-09-11","arxiv_id":"2409.07267","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-point-cloud-registration-with","slug":"unsupervised-point-cloud-registration-with","title":"Unsupervised Point Cloud Registration with Self-Distillation","date":"2024-09-11","arxiv_id":"2409.07558","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-self-supervised-learning-with","slug":"cross-modal-self-supervised-learning-with","title":"Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds","date":"2024-09-10","arxiv_id":"2409.06827","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-discrepancy-and-feature","slug":"distribution-discrepancy-and-feature","title":"Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection","date":"2024-09-09","arxiv_id":"2409.05425","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-survey-on-evidential-deep","slug":"a-comprehensive-survey-on-evidential-deep","title":"A Comprehensive Survey on Evidential Deep Learning and Its Applications","date":"2024-09-07","arxiv_id":"2409.04720","repositories_listed":1,"syntology":null},{"url":"/paper/3d-gp-lmvic-learning-based-multi-view-image","slug":"3d-gp-lmvic-learning-based-multi-view-image","title":"3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors","date":"2024-09-06","arxiv_id":"2409.04013","repositories_listed":1,"syntology":null},{"url":"/paper/developing-analyzing-and-evaluating-self","slug":"developing-analyzing-and-evaluating-self","title":"Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles","date":"2024-09-04","arxiv_id":"2409.03114","repositories_listed":1,"syntology":null},{"url":"/paper/tasar-transferable-attack-on-skeletal-action","slug":"tasar-transferable-attack-on-skeletal-action","title":"TASAR: Transfer-based Attack on Skeletal Action Recognition","date":"2024-09-04","arxiv_id":"2409.02483","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tasar-transferable-attack-on-skeletal-action#ran","syntology_url":"https://syntology.ai/paper/2409.02483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.02483"}},"official":{"repos":["yunfengdiao/Skeleton-Robustness-Benchmark"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/allweathernet-unified-image-enhancement-for","slug":"allweathernet-unified-image-enhancement-for","title":"AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions","date":"2024-09-03","arxiv_id":"2409.02045","repositories_listed":1,"syntology":null},{"url":"/paper/snapshot-towards-application-centered-models","slug":"snapshot-towards-application-centered-models","title":"Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments","date":"2024-09-03","arxiv_id":"2409.01971","repositories_listed":1,"syntology":null},{"url":"/paper/get-up-geometric-aware-depth-estimation-with","slug":"get-up-geometric-aware-depth-estimation-with","title":"GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling","date":"2024-09-02","arxiv_id":"2409.02720","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-vectorized-map-perception-with","slug":"enhancing-vectorized-map-perception-with","title":"Enhancing Vectorized Map Perception with Historical Rasterized Maps","date":"2024-09-01","arxiv_id":"2409.00620","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-vectorized-map-perception-with#ran","syntology_url":"https://syntology.ai/paper/2409.00620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00620"}},"official":{"repos":["hxmap/hrmapnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"205e227f60645d157b14c79cb16dbc53ce03f81782280f533c99d0b1091030ce","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}