{"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/22","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":22,"pages_in_order":61,"rows_per_page":100,"rows":[2101,2200],"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/21","next":"/task/autonomous-driving/papers/23","papers":[{"url":null,"slug":"towards-depth-foundation-model-recent-trends","title":"Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation","date":"2025-07-15","arxiv_id":"2507.11540","repositories_listed":0,"syntology":null},{"url":null,"slug":"3dgaa-realistic-and-robust-3d-gaussian-based","title":"3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving","date":"2025-07-14","arxiv_id":"2507.09993","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-aware-conversational-adas-with","title":"Scene-Aware Conversational ADAS with Generative AI for Real-Time Driver Assistance","date":"2025-07-14","arxiv_id":"2507.10500","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-of-feed-forward-3d-reconstruction-from","title":"Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT","date":"2025-07-11","arxiv_id":"2507.08448","repositories_listed":0,"syntology":null},{"url":null,"slug":"objectomaly-objectness-aware-refinement-for","title":"Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision","date":"2025-07-10","arxiv_id":"2507.07460","repositories_listed":0,"syntology":null},{"url":null,"slug":"3dgs-lsr-large-scale-relocation-for","title":"3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting","date":"2025-07-08","arxiv_id":"2507.05661","repositories_listed":0,"syntology":null},{"url":null,"slug":"lead-the-llm-enhanced-planning-system","title":"LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving","date":"2025-07-08","arxiv_id":"2507.05754","repositories_listed":0,"syntology":null},{"url":null,"slug":"tigaug-data-augmentation-for-testing-traffic","title":"TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems","date":"2025-07-08","arxiv_id":"2507.05932","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-solar-altitude-guided-scene","title":"Towards Solar Altitude Guided Scene Illumination","date":"2025-07-08","arxiv_id":"2507.05812","repositories_listed":0,"syntology":null},{"url":null,"slug":"nrseg-noise-resilient-learning-for-bev","title":"NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models","date":"2025-07-05","arxiv_id":"2507.04002","repositories_listed":0,"syntology":null},{"url":null,"slug":"fmocc-tpv-driven-flow-matching-for-3d","title":"FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model","date":"2025-07-03","arxiv_id":"2507.02250","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-gaussian-splatting-driven-multi-view","title":"3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation","date":"2025-07-02","arxiv_id":"2507.01367","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-based-realistic-safety-critical-driving","title":"LLM-based Realistic Safety-Critical Driving Video Generation","date":"2025-07-02","arxiv_id":"2507.01264","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-in-3d","title":"Out-of-distribution detection in 3D applications: a review","date":"2025-07-01","arxiv_id":"2507.00570","repositories_listed":0,"syntology":null},{"url":"/paper/world4drive-end-to-end-autonomous-driving-via","slug":"world4drive-end-to-end-autonomous-driving-via","title":"World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model","date":"2025-07-01","arxiv_id":"2507.00603","repositories_listed":0,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":9,"n_pointer_only":6,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/world4drive-end-to-end-autonomous-driving-via#ran","syntology_url":"https://syntology.ai/paper/2507.00603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.00603"}},"official":null}},{"url":null,"slug":"point-cloud-compression-and-objective-quality","title":"Point Cloud Compression and Objective Quality Assessment: A Survey","date":"2025-06-28","arxiv_id":"2506.22902","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-multi-modal-sensors-a-review-of","title":"Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles","date":"2025-06-27","arxiv_id":"2506.21885","repositories_listed":0,"syntology":null},{"url":null,"slug":"duet-dual-incremental-object-detection-via","title":"DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic","date":"2025-06-26","arxiv_id":"2506.21260","repositories_listed":0,"syntology":null},{"url":null,"slug":"goirl-graph-oriented-inverse-reinforcement","title":"GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction","date":"2025-06-26","arxiv_id":"2506.21121","repositories_listed":0,"syntology":null},{"url":null,"slug":"madrive-memory-augmented-driving-scene","title":"MADrive: Memory-Augmented Driving Scene Modeling","date":"2025-06-26","arxiv_id":"2506.21520","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam4d-segment-anything-in-camera-and-lidar","title":"SAM4D: Segment Anything in Camera and LiDAR Streams","date":"2025-06-26","arxiv_id":"2506.21547","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-realm-vision-language-model-based-robust","title":"V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling","date":"2025-06-26","arxiv_id":"2506.21041","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain2model-transfer-training-sensory-and","title":"Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher","date":"2025-06-25","arxiv_id":"2506.20834","repositories_listed":0,"syntology":null},{"url":null,"slug":"case-based-reasoning-augmented-large-language","title":"Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios","date":"2025-06-25","arxiv_id":"2506.20531","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-transformer-driven-6g-physical","title":"Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement","date":"2025-06-25","arxiv_id":"2506.20597","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-2d-to-3d-cognition-a-brief-survey-of","title":"From 2D to 3D Cognition: A Brief Survey of General World Models","date":"2025-06-25","arxiv_id":"2506.20134","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-multi-frame-integration-for","title":"Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos","date":"2025-06-25","arxiv_id":"2506.20550","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-uncertainty-quantification","title":"A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers","date":"2025-06-24","arxiv_id":"2506.19895","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-multi-sensor-fusion-perception","title":"A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects","date":"2025-06-24","arxiv_id":"2506.19769","repositories_listed":0,"syntology":null},{"url":null,"slug":"pevlm-parallel-encoding-for-vision-language","title":"PEVLM: Parallel Encoding for Vision-Language Models","date":"2025-06-24","arxiv_id":"2506.19651","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-vision-language-action-model","title":"Unified Vision-Language-Action Model","date":"2025-06-24","arxiv_id":"2506.19850","repositories_listed":0,"syntology":null},{"url":null,"slug":"drive-r1-bridging-reasoning-and-planning-in","title":"Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning","date":"2025-06-23","arxiv_id":"2506.18234","repositories_listed":0,"syntology":null},{"url":null,"slug":"tdacloud-point-cloud-recognition-using","title":"TDACloud: Point Cloud Recognition Using Topological Data Analysis","date":"2025-06-23","arxiv_id":"2506.18725","repositories_listed":0,"syntology":null},{"url":null,"slug":"usvtrack-usv-based-4d-radar-camera-tracking","title":"USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways","date":"2025-06-23","arxiv_id":"2506.18737","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-multiobject-tracking-with-neural","title":"Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models","date":"2025-06-22","arxiv_id":"2506.18124","repositories_listed":0,"syntology":null},{"url":null,"slug":"coherent-track-before-detect","title":"Coherent Track-Before-Detect","date":"2025-06-22","arxiv_id":"2506.18177","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-gaussian-splatting-for-fine-detailed","title":"3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene","date":"2025-06-21","arxiv_id":"2506.17636","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-based-multimodal-biometrics-for-detecting","title":"AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning","date":"2025-06-20","arxiv_id":"2506.17364","repositories_listed":0,"syntology":null},{"url":null,"slug":"drarl-disengagement-reason-augmented","title":"DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy","date":"2025-06-20","arxiv_id":"2506.16720","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-and-feature-guided-uncertainty","title":"Semantic and Feature Guided Uncertainty Quantification of Visual Localization for Autonomous Vehicles","date":"2025-06-18","arxiv_id":"2506.15851","repositories_listed":0,"syntology":null},{"url":null,"slug":"adrd-llm-driven-autonomous-driving-based-on","title":"ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems","date":"2025-06-17","arxiv_id":"2506.14299","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-geometric-hierarchy-fusion-an","title":"Cross-Modal Geometric Hierarchy Fusion: An Implicit-Submap Driven Framework for Resilient 3D Place Recognition","date":"2025-06-17","arxiv_id":"2506.14243","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-with-large-language-models","title":"Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems","date":"2025-06-17","arxiv_id":"2506.14096","repositories_listed":0,"syntology":null},{"url":null,"slug":"leader360v-the-large-scale-real-world-360","title":"Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment","date":"2025-06-17","arxiv_id":"2506.14271","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-safety-first-human-like-decision","title":"Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow","date":"2025-06-17","arxiv_id":"2506.14502","repositories_listed":0,"syntology":null},{"url":null,"slug":"findmeifyoucan-bringing-open-set-metrics-to","title":"FindMeIfYouCan: Bringing Open Set metrics to $\\textit{near} $, $ \\textit{far} $ and $\\textit{farther}$ Out-of-Distribution Object Detection","date":"2025-06-16","arxiv_id":"2506.14008","repositories_listed":0,"syntology":null},{"url":null,"slug":"reltopo-enhancing-relational-modeling-for","title":"RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning","date":"2025-06-16","arxiv_id":"2506.13553","repositories_listed":0,"syntology":null},{"url":null,"slug":"stage-a-stream-centric-generative-world-model","title":"STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation","date":"2025-06-16","arxiv_id":"2506.13138","repositories_listed":0,"syntology":null},{"url":null,"slug":"x-scene-large-scale-driving-scene-generation","title":"X-Scene: Large-Scale Driving Scene Generation with High Fidelity and Flexible Controllability","date":"2025-06-16","arxiv_id":"2506.13558","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-data-driven-and-physics-based-models","title":"Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation","date":"2025-06-15","arxiv_id":"2506.12862","repositories_listed":0,"syntology":null},{"url":null,"slug":"focalad-local-motion-planning-for-end-to-end","title":"FocalAD: Local Motion Planning for End-to-End Autonomous Driving","date":"2025-06-13","arxiv_id":"2506.11419","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphgsocc-semantic-geometric-graph","title":"GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction","date":"2025-06-13","arxiv_id":"2506.14825","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-natural-robustness-of-vision-language","title":"On the Natural Robustness of Vision-Language Models Against Visual Perception Attacks in Autonomous Driving","date":"2025-06-13","arxiv_id":"2506.11472","repositories_listed":0,"syntology":null},{"url":null,"slug":"teleoperated-driving-a-new-challenge-for-3d","title":"Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds","date":"2025-06-13","arxiv_id":"2506.11804","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-based-lifting-of-2d-object-detections","title":"Vision-based Lifting of 2D Object Detections for Automated Driving","date":"2025-06-13","arxiv_id":"2506.11839","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10317","title":"Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving","date":"2025-06-12","arxiv_id":"2506.10317","repositories_listed":0,"syntology":null},{"url":null,"slug":"lrslam-low-rank-representation-of-signed","title":"LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System","date":"2025-06-12","arxiv_id":"2506.10567","repositories_listed":0,"syntology":null},{"url":null,"slug":"poutine-vision-language-trajectory-pre","title":"Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving","date":"2025-06-12","arxiv_id":"2506.11234","repositories_listed":0,"syntology":null},{"url":null,"slug":"ad-2-bench-a-hierarchical-cot-benchmark-for","title":"AD^2-Bench: A Hierarchical CoT Benchmark for MLLM in Autonomous Driving under Adverse Conditions","date":"2025-06-11","arxiv_id":"2506.09557","repositories_listed":0,"syntology":null},{"url":null,"slug":"adv-bmt-bidirectional-motion-transformer-for","title":"Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation","date":"2025-06-11","arxiv_id":"2506.09485","repositories_listed":0,"syntology":null},{"url":null,"slug":"dyss-dynamic-queries-and-state-space-learning","title":"DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos","date":"2025-06-11","arxiv_id":"2506.10242","repositories_listed":0,"syntology":null},{"url":null,"slug":"odg-occupancy-prediction-using-dual-gaussians","title":"ODG: Occupancy Prediction Using Dual Gaussians","date":"2025-06-11","arxiv_id":"2506.09417","repositories_listed":0,"syntology":null},{"url":"/paper/resim-reliable-world-simulation-for","slug":"resim-reliable-world-simulation-for","title":"ReSim: Reliable World Simulation for Autonomous Driving","date":"2025-06-11","arxiv_id":"2506.09981","repositories_listed":0,"syntology":null},{"url":null,"slug":"roca-robust-cross-domain-end-to-end","title":"RoCA: Robust Cross-Domain End-to-End Autonomous Driving","date":"2025-06-11","arxiv_id":"2506.10145","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08459","title":"Diffusion Models for Safety Validation of Autonomous Driving Systems","date":"2025-06-10","arxiv_id":"2506.08459","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08533","title":"Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)","date":"2025-06-10","arxiv_id":"2506.08533","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-for-argoverse2-scenario","title":"Technical Report for Argoverse2 Scenario Mining Challenges on Iterative Error Correction and Spatially-Aware Prompting","date":"2025-06-10","arxiv_id":"2506.11124","repositories_listed":0,"syntology":null},{"url":null,"slug":"trajflow-multi-modal-motion-prediction-via","title":"TrajFlow: Multi-modal Motion Prediction via Flow Matching","date":"2025-06-10","arxiv_id":"2506.08541","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08149","title":"Ego-centric Learning of Communicative World Models for Autonomous Driving","date":"2025-06-09","arxiv_id":"2506.08149","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08228","title":"Scaling Laws of Motion Forecasting and Planning -- A Technical Report","date":"2025-06-09","arxiv_id":"2506.08228","repositories_listed":0,"syntology":null},{"url":null,"slug":"litevlm-a-low-latency-vision-language-model","title":"LiteVLM: A Low-Latency Vision-Language Model Inference Pipeline for Resource-Constrained Environments","date":"2025-06-09","arxiv_id":"2506.07416","repositories_listed":0,"syntology":null},{"url":null,"slug":"r3d2-realistic-3d-asset-insertion-via","title":"R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation","date":"2025-06-09","arxiv_id":"2506.07826","repositories_listed":0,"syntology":null},{"url":"/paper/recogdrive-a-reinforced-cognitive-framework","slug":"recogdrive-a-reinforced-cognitive-framework","title":"ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving","date":"2025-06-09","arxiv_id":"2506.08052","repositories_listed":0,"syntology":null},{"url":null,"slug":"spikesmoke-spiking-neural-networks-for","title":"SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding","date":"2025-06-09","arxiv_id":"2506.07737","repositories_listed":0,"syntology":null},{"url":null,"slug":"zerovo-visual-odometry-with-minimal-1","title":"ZeroVO: Visual Odometry with Minimal Assumptions","date":"2025-06-09","arxiv_id":"2506.08005","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-3d-gaussian-splatting-with","title":"Accelerating 3D Gaussian Splatting with Neural Sorting and Axis-Oriented Rasterization","date":"2025-06-08","arxiv_id":"2506.07069","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-mapping-for-evolving-scenes","title":"Gaussian Mapping for Evolving Scenes","date":"2025-06-07","arxiv_id":"2506.06909","repositories_listed":0,"syntology":null},{"url":null,"slug":"driveaction-a-benchmark-for-exploring-human","title":"DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models","date":"2025-06-06","arxiv_id":"2506.05667","repositories_listed":0,"syntology":null},{"url":null,"slug":"trajectory-entropy-modeling-game-state","title":"Trajectory Entropy: Modeling Game State Stability from Multimodality Trajectory Prediction","date":"2025-06-06","arxiv_id":"2506.05810","repositories_listed":0,"syntology":null},{"url":null,"slug":"perfecting-depth-uncertainty-aware","title":"Perfecting Depth: Uncertainty-Aware Enhancement of Metric Depth","date":"2025-06-05","arxiv_id":"2506.04612","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-labeling-enables-faster-vision","title":"Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving","date":"2025-06-05","arxiv_id":"2506.05442","repositories_listed":0,"syntology":null},{"url":null,"slug":"track-any-anomalous-object-a-granular-video-1","title":"Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline","date":"2025-06-05","arxiv_id":"2506.05175","repositories_listed":0,"syntology":null},{"url":null,"slug":"ad-ee-early-exiting-for-fast-and-reliable","title":"AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving","date":"2025-06-04","arxiv_id":"2506.05404","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-vehicle-lateral-control-using-deep","title":"Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration","date":"2025-06-04","arxiv_id":"2506.04040","repositories_listed":0,"syntology":null},{"url":null,"slug":"bevcalib-lidar-camera-calibration-via","title":"BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations","date":"2025-06-03","arxiv_id":"2506.02587","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-adaptive-cross-modal-generative","title":"Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission","date":"2025-06-03","arxiv_id":"2506.03211","repositories_listed":0,"syntology":null},{"url":null,"slug":"gara-sam-robustifying-segment-anything-model","title":"GaRA-SAM: Robustifying Segment Anything Model with Gated-Rank Adaptation","date":"2025-06-03","arxiv_id":"2506.02882","repositories_listed":0,"syntology":null},{"url":null,"slug":"hilo-high-level-object-fusion-for-autonomous","title":"HiLO: High-Level Object Fusion for Autonomous Driving using Transformers","date":"2025-06-03","arxiv_id":"2506.02554","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-acoustic-intelligence-for-automotive","title":"Embedded Acoustic Intelligence for Automotive Systems","date":"2025-06-02","arxiv_id":"2506.11071","repositories_listed":0,"syntology":null},{"url":null,"slug":"drivemind-a-dual-vlm-based-reinforcement","title":"DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving","date":"2025-06-01","arxiv_id":"2506.00819","repositories_listed":0,"syntology":null},{"url":null,"slug":"lora-as-a-flexible-framework-for-securing","title":"LoRA as a Flexible Framework for Securing Large Vision Systems","date":"2025-05-31","arxiv_id":"2506.00661","repositories_listed":0,"syntology":null},{"url":"/paper/using-diffusion-ensembles-to-estimate","slug":"using-diffusion-ensembles-to-estimate","title":"Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving","date":"2025-05-31","arxiv_id":"2506.00560","repositories_listed":0,"syntology":null},{"url":null,"slug":"road-responsibility-oriented-reward-design","title":"ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving","date":"2025-05-30","arxiv_id":"2505.24317","repositories_listed":0,"syntology":null},{"url":null,"slug":"s4-driver-scalable-self-supervised-driving","title":"S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation","date":"2025-05-30","arxiv_id":"2505.24139","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoregressive-meta-actions-for-unified","title":"Autoregressive Meta-Actions for Unified Controllable Trajectory Generation","date":"2025-05-29","arxiv_id":"2505.23612","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-based-generative-models-for-3d","title":"Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving","date":"2025-05-29","arxiv_id":"2505.23115","repositories_listed":0,"syntology":null},{"url":null,"slug":"hmad-advancing-e2e-driving-with-anchored","title":"HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring","date":"2025-05-29","arxiv_id":"2505.23129","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-the-calibration","title":"Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization","date":"2025-05-29","arxiv_id":"2505.23866","repositories_listed":0,"syntology":null},{"url":"/paper/from-failures-to-fixes-llm-driven-scenario","slug":"from-failures-to-fixes-llm-driven-scenario","title":"From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving","date":"2025-05-28","arxiv_id":"2505.22067","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-burst-encodable-time-of-flight","title":"Learnable Burst-Encodable Time-of-Flight Imaging for High-Fidelity Long-Distance Depth Sensing","date":"2025-05-28","arxiv_id":"2505.22025","repositories_listed":0,"syntology":null}],"record_sha256":"aeb9c1c2754cfda60518ac5369912c028ca7b77e8e010521f7fb8589b88323f7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}