{"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/25","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":25,"pages_in_order":61,"rows_per_page":100,"rows":[2401,2500],"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/24","next":"/task/autonomous-driving/papers/26","papers":[{"url":null,"slug":"interactionmap-improving-online-vectorized","title":"InteractionMap: Improving Online Vectorized HDMap Construction with Interaction","date":"2025-03-27","arxiv_id":"2503.21659","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graphs-as-world-models-for-semantic","title":"Knowledge Graphs as World Models for Semantic Material-Aware Obstacle Handling in Autonomous Vehicles","date":"2025-03-27","arxiv_id":"2503.21232","repositories_listed":0,"syntology":null},{"url":null,"slug":"accidentsim-generating-physically-realistic","title":"AccidentSim: Generating Physically Realistic Vehicle Collision Videos from Real-World Accident Reports","date":"2025-03-26","arxiv_id":"2503.20654","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolsplat-efficient-volume-based-gaussian","title":"EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis","date":"2025-03-26","arxiv_id":"2503.20168","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaia-2-a-controllable-multi-view-generative","title":"GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving","date":"2025-03-26","arxiv_id":"2503.20523","repositories_listed":0,"syntology":null},{"url":null,"slug":"omnidirectional-depth-aided-occupancy","title":"Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving","date":"2025-03-26","arxiv_id":"2504.01023","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-semantic-segmentation-enhancing","title":"Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding with Large Language Models for Advanced Vision Applications","date":"2025-03-25","arxiv_id":"2503.19276","repositories_listed":0,"syntology":null},{"url":null,"slug":"lenviz-a-high-resolution-low-exposure-night","title":"LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset","date":"2025-03-25","arxiv_id":"2503.19804","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-deep-reinforcement-learning-for-20","title":"Multi-Agent Deep Reinforcement Learning for Safe Autonomous Driving with RICS-Assisted MEC","date":"2025-03-25","arxiv_id":"2503.19418","repositories_listed":0,"syntology":null},{"url":"/paper/orion-a-holistic-end-to-end-autonomous","slug":"orion-a-holistic-end-to-end-autonomous","title":"ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation","date":"2025-03-25","arxiv_id":"2503.19755","repositories_listed":0,"syntology":null},{"url":null,"slug":"resilient-sensor-fusion-under-adverse-sensor","title":"Resilient Sensor Fusion under Adverse Sensor Failures via Multi-Modal Expert Fusion","date":"2025-03-25","arxiv_id":"2503.19776","repositories_listed":0,"syntology":null},{"url":null,"slug":"st-vlm-kinematic-instruction-tuning-for","title":"ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models","date":"2025-03-25","arxiv_id":"2503.19355","repositories_listed":0,"syntology":null},{"url":null,"slug":"aed-automatic-discovery-of-effective-and","title":"AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models","date":"2025-03-24","arxiv_id":"2503.20804","repositories_listed":0,"syntology":null},{"url":null,"slug":"agentspec-customizable-runtime-enforcement","title":"AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents","date":"2025-03-24","arxiv_id":"2503.18666","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-blocks-for-robust-and-effective-semi","title":"Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection","date":"2025-03-24","arxiv_id":"2503.18903","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-road-ahead-a-knowledge-graph","title":"Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving","date":"2025-03-24","arxiv_id":"2503.18730","repositories_listed":0,"syntology":null},{"url":null,"slug":"recondreamer-harmonizing-generative-and","title":"ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation","date":"2025-03-24","arxiv_id":"2503.18438","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-lane-detection-with-wavelet-enhanced","title":"Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling","date":"2025-03-24","arxiv_id":"2503.18631","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-a-neural-network-for-partially","title":"Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles","date":"2025-03-23","arxiv_id":"2503.18177","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-outage-for-edge-inference-systems","title":"Revisiting Outage for Edge Inference Systems","date":"2025-03-22","arxiv_id":"2504.03686","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-steering-estimation-with-semantic","title":"Enhancing Steering Estimation with Semantic-Aware GNNs","date":"2025-03-21","arxiv_id":"2503.17153","repositories_listed":0,"syntology":null},{"url":null,"slug":"hi-alps-an-experimental-robustness","title":"Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving","date":"2025-03-21","arxiv_id":"2503.17168","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-promote-autonomous-driving-with","title":"How to Promote Autonomous Driving with Evolving Technology: Business Strategy and Pricing Decision","date":"2025-03-21","arxiv_id":"2503.17174","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-livit-a-lidar-visual-thermal-dataset","title":"R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception","date":"2025-03-21","arxiv_id":"2503.17122","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-action-detection-model-compression","title":"Temporal Action Detection Model Compression by Progressive Block Drop","date":"2025-03-21","arxiv_id":"2503.16916","repositories_listed":0,"syntology":null},{"url":null,"slug":"autodrive-qa-automated-generation-of-multiple","title":"AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models","date":"2025-03-20","arxiv_id":"2503.15778","repositories_listed":0,"syntology":null},{"url":null,"slug":"nano-3d-metasurface-based-neural-depth","title":"Nano-3D: Metasurface-Based Neural Depth Imaging","date":"2025-03-20","arxiv_id":"2503.15770","repositories_listed":0,"syntology":null},{"url":null,"slug":"panoptic-cudal-technical-report-rural","title":"Panoptic-CUDAL Technical Report: Rural Australia Point Cloud Dataset in Rainy Conditions","date":"2025-03-20","arxiv_id":"2503.16378","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatstitch-visualizing-through-structures-via","title":"ChatStitch: Visualizing Through Structures via Surround-View Unsupervised Deep Image Stitching with Collaborative LLM-Agents","date":"2025-03-19","arxiv_id":"2503.14948","repositories_listed":0,"syntology":null},{"url":null,"slug":"drope-directional-rotary-position-embedding","title":"DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling","date":"2025-03-19","arxiv_id":"2503.15029","repositories_listed":0,"syntology":null},{"url":null,"slug":"gasp-unifying-geometric-and-semantic-self","title":"GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving","date":"2025-03-19","arxiv_id":"2503.15672","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-multimodal-driving-scenes-via-next","title":"Generating Multimodal Driving Scenes via Next-Scene Prediction","date":"2025-03-19","arxiv_id":"2503.14945","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmdt-decoding-the-trustworthiness-and-safety","title":"MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models","date":"2025-03-19","arxiv_id":"2503.14827","repositories_listed":0,"syntology":null},{"url":null,"slug":"semanticflow-a-self-supervised-framework-for","title":"SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments","date":"2025-03-19","arxiv_id":"2503.14837","repositories_listed":0,"syntology":null},{"url":null,"slug":"usam-net-a-u-net-based-network-for-improved","title":"USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network","date":"2025-03-19","arxiv_id":"2503.14950","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-dg-domain-generalization-for-vehicle-to","title":"V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception","date":"2025-03-19","arxiv_id":"2503.15435","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatbev-a-visual-language-model-that","title":"ChatBEV: A Visual Language Model that Understands BEV Maps","date":"2025-03-18","arxiv_id":"2503.13938","repositories_listed":0,"syntology":null},{"url":null,"slug":"cp-ncbf-a-conformal-prediction-based-approach","title":"CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions","date":"2025-03-18","arxiv_id":"2503.17395","repositories_listed":0,"syntology":null},{"url":null,"slug":"driving-behavior-recognition-via-self","title":"Driving behavior recognition via self-discovery learning","date":"2025-03-18","arxiv_id":"2503.14194","repositories_listed":0,"syntology":null},{"url":"/paper/psa-ssl-pose-and-size-aware-self-supervised","slug":"psa-ssl-pose-and-size-aware-self-supervised","title":"PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds","date":"2025-03-18","arxiv_id":"2503.13914","repositories_listed":0,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/psa-ssl-pose-and-size-aware-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2503.13914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13914"}},"official":null}},{"url":null,"slug":"rad-retrieval-augmented-decision-making-of","title":"RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving","date":"2025-03-18","arxiv_id":"2503.13861","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust3d-cil-robust-class-incremental","title":"Robust3D-CIL: Robust Class-Incremental Learning for 3D Perception","date":"2025-03-18","arxiv_id":"2503.13869","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpc-a-single-diffusion-model-for-point","title":"SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization","date":"2025-03-18","arxiv_id":"2503.14558","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-multi-agent","title":"A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives","date":"2025-03-17","arxiv_id":"2503.13415","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmapnet-improving-spatial-latent-structure","title":"AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction","date":"2025-03-17","arxiv_id":"2503.13430","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-3d-reconstruction-in","title":"Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey","date":"2025-03-17","arxiv_id":"2503.14537","repositories_listed":0,"syntology":null},{"url":null,"slug":"optipmb-enhancing-3d-multi-object-tracking","title":"OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering","date":"2025-03-17","arxiv_id":"2503.12968","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam2-for-image-and-video-segmentation-a","title":"SAM2 for Image and Video Segmentation: A Comprehensive Survey","date":"2025-03-17","arxiv_id":"2503.12781","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsealign-a-fully-sparse-framework-for","title":"SparseAlign: A Fully Sparse Framework for Cooperative Object Detection","date":"2025-03-17","arxiv_id":"2503.12982","repositories_listed":0,"syntology":null},{"url":null,"slug":"l2cocc-lightweight-camera-centric-semantic","title":"L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model","date":"2025-03-16","arxiv_id":"2503.12369","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-cloud-based-scene-segmentation-a-survey","title":"Point Cloud Based Scene Segmentation: A Survey","date":"2025-03-16","arxiv_id":"2503.12595","repositories_listed":0,"syntology":null},{"url":"/paper/diffad-a-unified-diffusion-modeling-approach","slug":"diffad-a-unified-diffusion-modeling-approach","title":"DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving","date":"2025-03-15","arxiv_id":"2503.12170","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-a-capability-driven","title":"A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving","date":"2025-03-14","arxiv_id":"2503.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-from-scene-embeddings-for-end","title":"Active Learning from Scene Embeddings for End-to-End Autonomous Driving","date":"2025-03-14","arxiv_id":"2503.11062","repositories_listed":0,"syntology":null},{"url":"/paper/centaur-robust-end-to-end-autonomous-driving","slug":"centaur-robust-end-to-end-autonomous-driving","title":"Centaur: Robust End-to-End Autonomous Driving with Test-Time Training","date":"2025-03-14","arxiv_id":"2503.11650","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynrsl-vlm-enhancing-autonomous-driving","title":"DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models","date":"2025-03-14","arxiv_id":"2503.11265","repositories_listed":0,"syntology":null},{"url":null,"slug":"industrial-grade-sensor-simulation-via","title":"Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation","date":"2025-03-14","arxiv_id":"2503.11731","repositories_listed":0,"syntology":null},{"url":null,"slug":"finetuning-generative-trajectory-model-with","title":"Finetuning Generative Trajectory Model with Reinforcement Learning from Human Feedback","date":"2025-03-13","arxiv_id":"2503.10434","repositories_listed":0,"syntology":null},{"url":null,"slug":"mudg-taming-multi-modal-diffusion-with","title":"MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction","date":"2025-03-13","arxiv_id":"2503.10604","repositories_listed":0,"syntology":null},{"url":null,"slug":"taiji-textual-anchoring-for-immunizing","title":"TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models","date":"2025-03-13","arxiv_id":"2503.10872","repositories_listed":0,"syntology":null},{"url":null,"slug":"tars-traffic-aware-radar-scene-flow","title":"TARS: Traffic-Aware Radar Scene Flow Estimation","date":"2025-03-13","arxiv_id":"2503.10210","repositories_listed":0,"syntology":null},{"url":null,"slug":"tgp-two-modal-occupancy-prediction-with-3d","title":"TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness","date":"2025-03-13","arxiv_id":"2503.09941","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlock-the-power-of-unlabeled-data-in","title":"Unlock the Power of Unlabeled Data in Language Driving Model","date":"2025-03-13","arxiv_id":"2503.10586","repositories_listed":0,"syntology":null},{"url":null,"slug":"cleverdistiller-simple-and-spatially","title":"CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation","date":"2025-03-12","arxiv_id":"2503.09878","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-domain-homogeneous-fusion-with-cross","title":"Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection","date":"2025-03-12","arxiv_id":"2503.08992","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-impact-of-synthetic-data-on","title":"Evaluating the Impact of Synthetic Data on Object Detection Tasks in Autonomous Driving","date":"2025-03-12","arxiv_id":"2503.09803","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-rendering-for-multimodal-autonomous","title":"Hybrid Rendering for Multimodal Autonomous Driving: Merging Neural and Physics-Based Simulation","date":"2025-03-12","arxiv_id":"2503.09464","repositories_listed":0,"syntology":null},{"url":null,"slug":"other-vehicle-trajectories-are-also-needed-a","title":"Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latant Space","date":"2025-03-12","arxiv_id":"2503.09215","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-interactive-multimodal-trajectory","title":"Post-interactive Multimodal Trajectory Prediction for Autonomous Driving","date":"2025-03-12","arxiv_id":"2503.09366","repositories_listed":0,"syntology":null},{"url":null,"slug":"fasionad-integrating-high-level-instruction","title":"FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback","date":"2025-03-11","arxiv_id":"2503.08162","repositories_listed":0,"syntology":null},{"url":null,"slug":"jisam-alleviate-labeling-burden-and-corner","title":"JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data","date":"2025-03-11","arxiv_id":"2503.08422","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-automotive-radar-with-lidar-and","title":"Simulating Automotive Radar with Lidar and Camera Inputs","date":"2025-03-11","arxiv_id":"2503.08068","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulator-ensembles-for-trustworthy","title":"Simulator Ensembles for Trustworthy Autonomous Driving Testing","date":"2025-03-11","arxiv_id":"2503.08936","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-co-design-of-communication","title":"Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems","date":"2025-03-11","arxiv_id":"2503.08661","repositories_listed":0,"syntology":null},{"url":null,"slug":"catplan-loss-based-collision-prediction-in","title":"CATPlan: Loss-based Collision Prediction in End-to-End Autonomous Driving","date":"2025-03-10","arxiv_id":"2503.07425","repositories_listed":0,"syntology":null},{"url":null,"slug":"combating-partial-perception-deficit-in","title":"Combating Partial Perception Deficit in Autonomous Driving with Multimodal LLM Commonsense","date":"2025-03-10","arxiv_id":"2503.07020","repositories_listed":0,"syntology":null},{"url":null,"slug":"cot-drive-efficient-motion-forecasting-for","title":"CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting","date":"2025-03-10","arxiv_id":"2503.07234","repositories_listed":0,"syntology":null},{"url":null,"slug":"gm-moe-low-light-enhancement-with-gated","title":"GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts","date":"2025-03-10","arxiv_id":"2503.07417","repositories_listed":0,"syntology":null},{"url":null,"slug":"histrackmap-global-vectorized-high-definition","title":"HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking","date":"2025-03-10","arxiv_id":"2503.07168","repositories_listed":0,"syntology":null},{"url":null,"slug":"lego-motion-learning-enhanced-grids-with","title":"LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction","date":"2025-03-10","arxiv_id":"2503.07367","repositories_listed":0,"syntology":null},{"url":null,"slug":"robusto-1-dataset-comparing-humans-and-vlms","title":"Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru","date":"2025-03-10","arxiv_id":"2503.07587","repositories_listed":0,"syntology":null},{"url":null,"slug":"rs2v-l-vehicle-mounted-lidar-data-generation","title":"RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations","date":"2025-03-10","arxiv_id":"2503.07085","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-triplane-transformers-as-occupancy","title":"Temporal Triplane Transformers as Occupancy World Models","date":"2025-03-10","arxiv_id":"2503.07338","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-please-pixelshap-reveals-what","title":"Attention, Please! PixelSHAP Reveals What Vision-Language Models Actually Focus On","date":"2025-03-09","arxiv_id":"2503.06670","repositories_listed":0,"syntology":null},{"url":null,"slug":"axispose-model-free-matching-free-single-shot","title":"AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generation","date":"2025-03-09","arxiv_id":"2503.06660","repositories_listed":0,"syntology":null},{"url":null,"slug":"coda-4dgs-dynamic-gaussian-splatting-with","title":"CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving","date":"2025-03-09","arxiv_id":"2503.06744","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-safety-cognition-capability-in","title":"Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving","date":"2025-03-09","arxiv_id":"2503.06497","repositories_listed":0,"syntology":null},{"url":null,"slug":"future-aware-interaction-network-for-motion","title":"Future-Aware Interaction Network For Motion Forecasting","date":"2025-03-09","arxiv_id":"2503.06565","repositories_listed":0,"syntology":null},{"url":"/paper/ov-scan-semantically-consistent-alignment-for","slug":"ov-scan-semantically-consistent-alignment-for","title":"OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection","date":"2025-03-09","arxiv_id":"2503.06435","repositories_listed":0,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ov-scan-semantically-consistent-alignment-for#ran","syntology_url":"https://syntology.ai/paper/2503.06435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.06435"}},"official":null}},{"url":null,"slug":"steerable-pyramid-weighted-loss-multi-scale","title":"Steerable Pyramid Weighted Loss: Multi-Scale Adaptive Weighting for Semantic Segmentation","date":"2025-03-09","arxiv_id":"2503.06604","repositories_listed":0,"syntology":null},{"url":null,"slug":"acam-kd-adaptive-and-cooperative-attention","title":"ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation","date":"2025-03-08","arxiv_id":"2503.06307","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-autonomous-vehicle-intelligence","title":"Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection","date":"2025-03-08","arxiv_id":"2503.06313","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-dataset-to-real-world-general-3d-object","title":"From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning","date":"2025-03-08","arxiv_id":"2503.06282","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-drive-by-imitating-surrounding","title":"Learning to Drive by Imitating Surrounding Vehicles","date":"2025-03-08","arxiv_id":"2503.05997","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-centric-world-model-for-language","title":"Object-Centric World Model for Language-Guided Manipulation","date":"2025-03-08","arxiv_id":"2503.06170","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-lanes-and-points-in-complex","title":"Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection","date":"2025-03-08","arxiv_id":"2503.06237","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-anything-even-occluded","title":"Segment Anything, Even Occluded","date":"2025-03-08","arxiv_id":"2503.06261","repositories_listed":0,"syntology":null},{"url":null,"slug":"transparking-a-dual-decoder-transformer","title":"TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking","date":"2025-03-08","arxiv_id":"2503.06071","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-based-3d-semantic-scene-completion-via","title":"Vision-based 3D Semantic Scene Completion via Capture Dynamic Representations","date":"2025-03-08","arxiv_id":"2503.06222","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-for-extending-automotive","title":"A Hybrid Approach for Extending Automotive Radar Operation to NLOS Urban Scenarios","date":"2025-03-07","arxiv_id":"2503.05413","repositories_listed":0,"syntology":null}],"record_sha256":"cda975e33d76227430ec1a0977610c65e76ea50445ed3c0e63e0c2224ee27086","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}