{"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/33","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":33,"pages_in_order":61,"rows_per_page":100,"rows":[3201,3300],"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/32","next":"/task/autonomous-driving/papers/34","papers":[{"url":null,"slug":"explainable-ai-for-enhancing-efficiency-of-dl","title":"Explainable AI for Enhancing Efficiency of DL-based Channel Estimation","date":"2024-07-09","arxiv_id":"2407.07009","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-camera-encoder-designs-for","title":"Exploring Camera Encoder Designs for Autonomous Driving Perception","date":"2024-07-09","arxiv_id":"2407.07276","repositories_listed":0,"syntology":null},{"url":null,"slug":"less-is-more-efficient-brain-inspired","title":"Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction","date":"2024-07-09","arxiv_id":"2407.07020","repositories_listed":0,"syntology":null},{"url":null,"slug":"vqa-diff-exploiting-vqa-and-diffusion-for","title":"VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving","date":"2024-07-09","arxiv_id":"2407.06516","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-3d-object-detection-with-semantic","title":"Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework","date":"2024-07-08","arxiv_id":"2407.05769","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-few-shot-in-context-learning-for","title":"Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition","date":"2024-07-08","arxiv_id":"2407.05814","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-safety-in-autonomous-driving","title":"Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation","date":"2024-07-08","arxiv_id":"2407.06317","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-vision-language-models-with-scene","title":"Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding","date":"2024-07-08","arxiv_id":"2407.05910","repositories_listed":0,"syntology":null},{"url":null,"slug":"genfollower-enhancing-car-following","title":"GenFollower: Enhancing Car-Following Prediction with Large Language Models","date":"2024-07-08","arxiv_id":"2407.05611","repositories_listed":0,"syntology":null},{"url":null,"slug":"mstf-multiscale-transformer-for-incomplete","title":"MSTF: Multiscale Transformer for Incomplete Trajectory Prediction","date":"2024-07-08","arxiv_id":"2407.05671","repositories_listed":0,"syntology":null},{"url":null,"slug":"rhrsegnet-relighting-high-resolution-night","title":"RHRSegNet: Relighting High-Resolution Night-Time Semantic Segmentation","date":"2024-07-08","arxiv_id":"2407.06016","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-trigger-detection-and","title":"Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense","date":"2024-07-07","arxiv_id":"2407.05396","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-reflected-objects-a-benchmark","title":"Tracking Reflected Objects: A Benchmark","date":"2024-07-07","arxiv_id":"2407.05235","repositories_listed":0,"syntology":null},{"url":null,"slug":"sid-stereo-image-dataset-for-autonomous","title":"SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions","date":"2024-07-06","arxiv_id":"2407.04908","repositories_listed":0,"syntology":null},{"url":null,"slug":"dance-of-the-ads-orchestrating-failures","title":"Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing","date":"2024-07-05","arxiv_id":"2407.04359","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-the-image-correction-pipeline-for","title":"Optimizing the image correction pipeline for pedestrian detection in the thermal-infrared domain","date":"2024-07-05","arxiv_id":"2407.04484","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-applications-and-prospects-of-event","title":"Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey","date":"2024-07-05","arxiv_id":"2407.04277","repositories_listed":0,"syntology":null},{"url":null,"slug":"timeldm-latent-diffusion-model-for","title":"TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation","date":"2024-07-05","arxiv_id":"2407.04211","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-stable-3d-object-detection","title":"Towards Stable 3D Object Detection","date":"2024-07-05","arxiv_id":"2407.04305","repositories_listed":0,"syntology":null},{"url":null,"slug":"behavioural-gap-assessment-of-human-vehicle","title":"Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving","date":"2024-07-04","arxiv_id":"2407.04070","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-radiometric-correction-based-optical","title":"A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban Scenes","date":"2024-07-03","arxiv_id":"2407.02830","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-fusion-and-task-guided-embedding","title":"Efficient Fusion and Task Guided Embedding for End-to-end Autonomous Driving","date":"2024-07-03","arxiv_id":"2407.02878","repositories_listed":0,"syntology":null},{"url":null,"slug":"autosplat-constrained-gaussian-splatting-for","title":"AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction","date":"2024-07-02","arxiv_id":"2407.02598","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-edge-terminal-collaborative-aigc-for","title":"Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving","date":"2024-07-02","arxiv_id":"2407.01956","repositories_listed":0,"syntology":null},{"url":null,"slug":"dajc-a-2-02mw-50mbps-direct-analog-to-mjpeg","title":"dAJC: A 2.02mW 50Mbps Direct Analog to MJPEG Converter for Video Sensor Node using Low-Noise Switched Capacitor MAC-Quantizer with Auto-Calibration and Sparsity-Aware ADC","date":"2024-07-02","arxiv_id":"2407.11023","repositories_listed":0,"syntology":null},{"url":null,"slug":"dm3d-distortion-minimized-weight-pruning-for","title":"DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection","date":"2024-07-02","arxiv_id":"2407.02098","repositories_listed":0,"syntology":null},{"url":null,"slug":"flowtrack-point-level-flow-network-for-3d","title":"FlowTrack: Point-level Flow Network for 3D Single Object Tracking","date":"2024-07-02","arxiv_id":"2407.01959","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-on-reliable-and-safe-occupancy-grid","title":"Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots","date":"2024-07-02","arxiv_id":"2407.02197","repositories_listed":0,"syntology":null},{"url":null,"slug":"acceleration-method-for-generating-perception","title":"Acceleration method for generating perception failure scenarios based on editing Markov process","date":"2024-07-01","arxiv_id":"2407.00980","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-for-adverse","title":"Deep Reinforcement Learning for Adverse Garage Scenario Generation","date":"2024-07-01","arxiv_id":"2407.01333","repositories_listed":0,"syntology":null},{"url":null,"slug":"falcon-frequency-adjoint-link-with-continuous","title":"FALCON: Frequency Adjoint Link with CONtinuous Density Mask for Fast Single Image Dehazing","date":"2024-07-01","arxiv_id":"2407.00972","repositories_listed":0,"syntology":null},{"url":null,"slug":"hgnet-a-hierarchical-feature-guided-network","title":"HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction","date":"2024-07-01","arxiv_id":"2407.01097","repositories_listed":0,"syntology":null},{"url":null,"slug":"let-hybrid-a-path-planner-obey-traffic-rules","title":"Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework","date":"2024-07-01","arxiv_id":"2407.01216","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-trust-dynamics-with-dynamic-sem-in","title":"Predicting Trust Dynamics with Dynamic SEM in Human-AI Cooperation","date":"2024-07-01","arxiv_id":"2407.01752","repositories_listed":0,"syntology":null},{"url":null,"slug":"tokenize-the-world-into-object-level","title":"Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving","date":"2024-07-01","arxiv_id":"2407.00959","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rule-based-behaviour-planner-for-autonomous","title":"A Rule-Based Behaviour Planner for Autonomous Driving","date":"2024-06-29","arxiv_id":"2407.00460","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-depth-estimation-methods","title":"Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey","date":"2024-06-28","arxiv_id":"2406.19675","repositories_listed":0,"syntology":null},{"url":null,"slug":"streammotp-streaming-and-unified-framework","title":"StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction","date":"2024-06-28","arxiv_id":"2406.19844","repositories_listed":0,"syntology":null},{"url":null,"slug":"bico-fusion-bidirectional-complementary-lidar","title":"BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection","date":"2024-06-27","arxiv_id":"2406.19048","repositories_listed":0,"syntology":null},{"url":null,"slug":"xld-a-cross-lane-dataset-for-benchmarking","title":"XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis","date":"2024-06-26","arxiv_id":"2406.18360","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-autonomous-driving-without-costly","title":"End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation","date":"2024-06-25","arxiv_id":"2406.17680","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-guided-outdoor-lidar-perception-quality","title":"Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving","date":"2024-06-25","arxiv_id":"2406.17265","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-autonomous-driving-image","title":"Optimization of Autonomous Driving Image Detection Based on RFAConv and Triplet Attention","date":"2024-06-25","arxiv_id":"2407.09530","repositories_listed":0,"syntology":null},{"url":null,"slug":"querying-labeled-time-series-data-with","title":"Querying Labeled Time Series Data with Scenario Programs","date":"2024-06-25","arxiv_id":"2406.17627","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-4v-explorations-mining-autonomous-driving","title":"GPT-4V Explorations: Mining Autonomous Driving","date":"2024-06-24","arxiv_id":"2406.16817","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-training-datasets-for-machine","title":"Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection","date":"2024-06-21","arxiv_id":"2406.15268","repositories_listed":0,"syntology":null},{"url":null,"slug":"futurenet-lof-joint-trajectory-prediction-and","title":"FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding","date":"2024-06-20","arxiv_id":"2406.14422","repositories_listed":0,"syntology":null},{"url":null,"slug":"posebench-benchmarking-the-robustness-of-pose","title":"PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions","date":"2024-06-20","arxiv_id":"2406.14367","repositories_listed":0,"syntology":null},{"url":null,"slug":"preferential-multi-objective-bayesian","title":"Preferential Multi-Objective Bayesian Optimization","date":"2024-06-20","arxiv_id":"2406.14699","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-use-of-multimodal-large-language-models","title":"The Use of Multimodal Large Language Models to Detect Objects from Thermal Images: Transportation Applications","date":"2024-06-20","arxiv_id":"2406.13898","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-and-self-supervision-in-single","title":"Uncertainty and Self-Supervision in Single-View Depth","date":"2024-06-20","arxiv_id":"2406.14226","repositories_listed":0,"syntology":null},{"url":null,"slug":"urban-focused-multi-task-offline","title":"Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing","date":"2024-06-20","arxiv_id":"2406.14054","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-your-hd-map-constructor-reliable-under","title":"Is Your HD Map Constructor Reliable under Sensor Corruptions?","date":"2024-06-18","arxiv_id":"2406.12214","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-adaptive-anomaly-detection-for-defect","title":"Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly","date":"2024-06-18","arxiv_id":"2406.12698","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-efficient-imitative-multi-token","title":"Physics-informed Imitative Reinforcement Learning for Real-world Driving","date":"2024-06-18","arxiv_id":"2407.02508","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-first-physical-world-trajectory-prediction","title":"A First Physical-World Trajectory Prediction Attack via LiDAR-induced Deceptions in Autonomous Driving","date":"2024-06-17","arxiv_id":"2406.11707","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-marl-for-platoon","title":"Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs","date":"2024-06-17","arxiv_id":"2406.11653","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-reinforcement-learning-with-2","title":"Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms","date":"2024-06-17","arxiv_id":"2406.11481","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossfusor-a-cross-attention-transformer","title":"Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction","date":"2024-06-17","arxiv_id":"2406.11941","repositories_listed":0,"syntology":null},{"url":null,"slug":"distillnerf-perceiving-3d-scenes-from-single","title":"DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features","date":"2024-06-17","arxiv_id":"2406.12095","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-adaptation-for-time-constrained-1","title":"Model Adaptation for Time Constrained Embodied Control","date":"2024-06-17","arxiv_id":"2406.11128","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsedet-a-simple-and-effective-framework","title":"SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection","date":"2024-06-16","arxiv_id":"2406.10907","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-and-evolving-reward-functions-for","title":"Generating and Evolving Reward Functions for Highway Driving with Large Language Models","date":"2024-06-15","arxiv_id":"2406.10540","repositories_listed":0,"syntology":null},{"url":null,"slug":"genmm-geometrically-and-temporally-consistent","title":"GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR","date":"2024-06-15","arxiv_id":"2406.10722","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-using-oriented-window","title":"Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition","date":"2024-06-15","arxiv_id":"2406.10712","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-with-adaptive-world-models-for","title":"Planning with Adaptive World Models for Autonomous Driving","date":"2024-06-15","arxiv_id":"2406.10714","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-communication-for-edge-intelligence","title":"Semantic Communication for Edge Intelligence Enabled Autonomous Driving System","date":"2024-06-15","arxiv_id":"2406.10606","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparseradnet-sparse-perception-neural-network","title":"SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data","date":"2024-06-15","arxiv_id":"2406.10600","repositories_listed":0,"syntology":null},{"url":null,"slug":"galibr-targetless-lidar-camera-extrinsic","title":"Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization","date":"2024-06-14","arxiv_id":"2406.11599","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapvision-cvpr-2024-autonomous-grand","title":"MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report","date":"2024-06-14","arxiv_id":"2406.10125","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-on-edge-detection-of-lidar-images","title":"Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology","date":"2024-06-14","arxiv_id":"2406.09773","repositories_listed":0,"syntology":null},{"url":null,"slug":"cimrl-combining-imitiation-and-reinforcement","title":"CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving","date":"2024-06-13","arxiv_id":"2406.08878","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modality-program-representation","title":"Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis","date":"2024-06-13","arxiv_id":"2406.09606","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-visual-question-answering-models","title":"Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns","date":"2024-06-13","arxiv_id":"2406.09203","repositories_listed":0,"syntology":null},{"url":null,"slug":"simgen-simulator-conditioned-driving-scene","title":"SimGen: Simulator-conditioned Driving Scene Generation","date":"2024-06-13","arxiv_id":"2406.09386","repositories_listed":0,"syntology":null},{"url":"/paper/lanecpp-continuous-3d-lane-detection-using-1","slug":"lanecpp-continuous-3d-lane-detection-using-1","title":"LaneCPP: Continuous 3D Lane Detection using Physical Priors","date":"2024-06-12","arxiv_id":"2406.08381","repositories_listed":0,"syntology":null},{"url":null,"slug":"lisd-an-efficient-multi-task-learning","title":"LiSD: An Efficient Multi-Task Learning Framework for LiDAR Segmentation and Detection","date":"2024-06-11","arxiv_id":"2406.07023","repositories_listed":0,"syntology":null},{"url":null,"slug":"panossc-exploring-monocular-panoptic-3d-scene","title":"PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving","date":"2024-06-11","arxiv_id":"2406.07037","repositories_listed":0,"syntology":null},{"url":null,"slug":"plt-d3-a-high-fidelity-dynamic-driving","title":"PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow","date":"2024-06-11","arxiv_id":"2406.07667","repositories_listed":0,"syntology":null},{"url":null,"slug":"dualad-disentangling-the-dynamic-and-static","title":"DualAD: Disentangling the Dynamic and Static World for End-to-End Driving","date":"2024-06-10","arxiv_id":"2406.06264","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-video-anomaly-detection-for-anomalous","title":"Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving","date":"2024-06-10","arxiv_id":"2406.06423","repositories_listed":0,"syntology":null},{"url":null,"slug":"umad-unsupervised-mask-level-anomaly","title":"UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving","date":"2024-06-10","arxiv_id":"2406.06370","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-superalignment-framework-in-autonomous","title":"A Superalignment Framework in Autonomous Driving with Large Language Models","date":"2024-06-09","arxiv_id":"2406.05651","repositories_listed":0,"syntology":null},{"url":null,"slug":"controlloc-physical-world-hijacking-attack-on","title":"ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving","date":"2024-06-09","arxiv_id":"2406.05810","repositories_listed":0,"syntology":null},{"url":null,"slug":"slowperception-physical-world-latency-attack","title":"SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving","date":"2024-06-09","arxiv_id":"2406.05800","repositories_listed":0,"syntology":null},{"url":null,"slug":"citycraft-a-real-crafter-for-3d-city","title":"CityCraft: A Real Crafter for 3D City Generation","date":"2024-06-07","arxiv_id":"2406.04983","repositories_listed":0,"syntology":null},{"url":null,"slug":"fragile-model-watermarking-a-comprehensive","title":"A Survey of Fragile Model Watermarking","date":"2024-06-07","arxiv_id":"2406.04809","repositories_listed":0,"syntology":null},{"url":null,"slug":"detra-a-unified-model-for-object-detection","title":"DeTra: A Unified Model for Object Detection and Trajectory Forecasting","date":"2024-06-06","arxiv_id":"2406.04426","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-localization-with-semantics-map-for","title":"Monocular Localization with Semantics Map for Autonomous Vehicles","date":"2024-06-06","arxiv_id":"2406.03835","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-autonomous-driving-for-safety-a","title":"Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF","date":"2024-06-06","arxiv_id":"2406.04481","repositories_listed":0,"syntology":null},{"url":null,"slug":"step-out-and-seek-around-on-warm-start","title":"Step Out and Seek Around: On Warm-Start Training with Incremental Data","date":"2024-06-06","arxiv_id":"2406.04484","repositories_listed":0,"syntology":null},{"url":null,"slug":"ad-h-autonomous-driving-with-hierarchical","title":"AD-H: Autonomous Driving with Hierarchical Agents","date":"2024-06-05","arxiv_id":"2406.03474","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-automotive-object-detection-via-rgb","title":"Enhanced Automotive Object Detection via RGB-D Fusion in a DiffusionDet Framework","date":"2024-06-05","arxiv_id":"2406.03129","repositories_listed":0,"syntology":null},{"url":null,"slug":"polarization-wavefront-lidar-learning-large","title":"Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts","date":"2024-06-05","arxiv_id":"2406.03461","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-visual-alignment-for-zero-shot","title":"Prompt-based Visual Alignment for Zero-shot Policy Transfer","date":"2024-06-05","arxiv_id":"2406.03250","repositories_listed":0,"syntology":null},{"url":null,"slug":"situation-monitor-diversity-driven-zero-shot","title":"Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection","date":"2024-06-05","arxiv_id":"2406.03188","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-wireless-communications-for","title":"Task-Oriented Wireless Communications for Collaborative Perception in Intelligent Unmanned Systems","date":"2024-06-05","arxiv_id":"2406.03086","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-of-neural-network-calibration","title":"Decoupling of neural network calibration measures","date":"2024-06-04","arxiv_id":"2406.02411","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-real-world-map-change","title":"Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models","date":"2024-06-04","arxiv_id":"2406.01961","repositories_listed":0,"syntology":null},{"url":null,"slug":"radar-spectra-language-model-for-automotive","title":"Radar Spectra-Language Model for Automotive Scene Parsing","date":"2024-06-04","arxiv_id":"2406.02158","repositories_listed":0,"syntology":null}],"record_sha256":"86e6b9fe1e8d998f3e075cfc171176d9a4eaeb2bcecb9065573789c2627b1e3b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}