{"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/39","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":39,"pages_in_order":61,"rows_per_page":100,"rows":[3801,3900],"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/38","next":"/task/autonomous-driving/papers/40","papers":[{"url":null,"slug":"pixel-level-clustering-network-for","title":"Pixel-Level Clustering Network for Unsupervised Image Segmentation","date":"2023-10-24","arxiv_id":"2310.16234","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-multi-modal-3d-scene","title":"Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation","date":"2023-10-24","arxiv_id":"2310.15676","repositories_listed":0,"syntology":null},{"url":null,"slug":"dice-diverse-diffusion-model-with-scoring-for","title":"DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction","date":"2023-10-23","arxiv_id":"2310.14570","repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-map-and-agent-geometry-for","title":"Equivariant Map and Agent Geometry for Autonomous Driving Motion Prediction","date":"2023-10-21","arxiv_id":"2310.13922","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-driving-behavior-for-autonomous","title":"Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer","date":"2023-10-21","arxiv_id":"2310.13906","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-bit-byzantine-tolerant-distributed","title":"One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation","date":"2023-10-18","arxiv_id":"2310.11998","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-experience-classification-for-training","title":"Using Experience Classification for Training Non-Markovian Tasks","date":"2023-10-18","arxiv_id":"2310.11678","repositories_listed":0,"syntology":null},{"url":null,"slug":"dorec-decomposed-object-reconstruction","title":"DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features","date":"2023-10-17","arxiv_id":"2310.11092","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-following-control-of-automated-vehicle","title":"Path Following Control of Automated Vehicle Considering Uncertainties and Disturbances with Parametric Varying","date":"2023-10-17","arxiv_id":"2310.10925","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-object-query-initialization-for-3d","title":"Multimodal Object Query Initialization for 3D Object Detection","date":"2023-10-16","arxiv_id":"2310.10353","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-traffic-sign-detection-a-case-study","title":"Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood","date":"2023-10-14","arxiv_id":"2310.09630","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-multi-modal-3d-semantic","title":"Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving","date":"2023-10-13","arxiv_id":"2310.08826","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-invariance-for-reference","title":"Data-driven Invariance for Reference Governors","date":"2023-10-12","arxiv_id":"2310.08679","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphalign-enhancing-accurate-feature-1","title":"GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection","date":"2023-10-12","arxiv_id":"2310.08261","repositories_listed":0,"syntology":null},{"url":null,"slug":"heightformer-a-multilevel-interaction-and","title":"HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images","date":"2023-10-12","arxiv_id":"2310.07995","repositories_listed":0,"syntology":null},{"url":null,"slug":"if-our-aim-is-to-build-morality-into-an","title":"If our aim is to build morality into an artificial agent, how might we begin to go about doing so?","date":"2023-10-12","arxiv_id":"2310.08295","repositories_listed":0,"syntology":null},{"url":null,"slug":"nsm4d-neural-scene-model-based-online-4d","title":"NSM4D: Neural Scene Model Based Online 4D Point Cloud Sequence Understanding","date":"2023-10-12","arxiv_id":"2310.08326","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-power-assessment-of-cnn-packages","title":"Performance/power assessment of CNN packages on embedded automotive platforms","date":"2023-10-12","arxiv_id":"2310.08401","repositories_listed":0,"syntology":null},{"url":null,"slug":"receive-reason-and-react-drive-as-you-say","title":"Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles","date":"2023-10-12","arxiv_id":"2310.08034","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-the-placement-of-roadside-lidars-1","title":"Optimizing the Placement of Roadside LiDARs for Autonomous Driving","date":"2023-10-11","arxiv_id":"2310.07247","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-by-construction-autonomous-vehicle","title":"Safe-by-Construction Autonomous Vehicle Overtaking using Control Barrier Functions and Model Predictive Control","date":"2023-10-10","arxiv_id":"2310.06553","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-guided-ray-augmentation-for-neural","title":"HarmonicNeRF: Geometry-Informed Synthetic View Augmentation for 3D Scene Reconstruction in Driving Scenarios","date":"2023-10-09","arxiv_id":"2310.05483","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-object-detection-and-re-identification","title":"Joint object detection and re-identification for 3D obstacle multi-camera systems","date":"2023-10-09","arxiv_id":"2310.05785","repositories_listed":0,"syntology":null},{"url":null,"slug":"layout-sequence-prediction-from-noisy-mobile","title":"Layout Sequence Prediction From Noisy Mobile Modality","date":"2023-10-09","arxiv_id":"2310.06138","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-upernet-and-convnext-for-contrails","title":"Combining UPerNet and ConvNeXt for Contrails Identification to reduce Global Warming","date":"2023-10-07","arxiv_id":"2310.04808","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamicbev-leveraging-dynamic-queries-and","title":"QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied Contexts","date":"2023-10-07","arxiv_id":"2310.05989","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-dynamic-and-small-objects-refinement","title":"Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation","date":"2023-10-07","arxiv_id":"2310.04747","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffprompter-differentiable-implicit-visual","title":"DiffPrompter: Differentiable Implicit Visual Prompts for Semantic-Segmentation in Adverse Conditions","date":"2023-10-06","arxiv_id":"2310.04181","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-cooperative-perception-for-autonomous","title":"V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges","date":"2023-10-05","arxiv_id":"2310.03525","repositories_listed":0,"syntology":null},{"url":null,"slug":"languagempc-large-language-models-as-decision","title":"LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving","date":"2023-10-04","arxiv_id":"2310.03026","repositories_listed":0,"syntology":null},{"url":null,"slug":"rsrd-a-road-surface-reconstruction-dataset","title":"RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving","date":"2023-10-03","arxiv_id":"2310.02262","repositories_listed":0,"syntology":null},{"url":null,"slug":"drivegpt4-interpretable-end-to-end-autonomous","title":"DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model","date":"2023-10-02","arxiv_id":"2310.01412","repositories_listed":0,"syntology":null},{"url":null,"slug":"elastic-interaction-energy-loss-for-traffic","title":"Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception","date":"2023-10-02","arxiv_id":"2310.01449","repositories_listed":0,"syntology":null},{"url":null,"slug":"every-dataset-counts-scaling-up-monocular-3d","title":"Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training","date":"2023-10-02","arxiv_id":"2310.00920","repositories_listed":0,"syntology":null},{"url":null,"slug":"ls-vos-identifying-outliers-in-3d-object","title":"LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis","date":"2023-10-02","arxiv_id":"2310.00952","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-3d-object-detection-in-rainy","title":"Towards Robust 3D Object Detection In Rainy Conditions","date":"2023-10-02","arxiv_id":"2310.00944","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-for-autonomous-6","title":"Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation","date":"2023-09-30","arxiv_id":"2310.08595","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmpi-a-flexible-radiance-field-representation","title":"MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending","date":"2023-09-30","arxiv_id":"2310.00249","repositories_listed":0,"syntology":null},{"url":null,"slug":"monogae-roadside-monocular-3d-object","title":"MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings","date":"2023-09-30","arxiv_id":"2310.00400","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-deep-learning-techniques-for-2","title":"A Survey on Deep Learning Techniques for Action Anticipation","date":"2023-09-29","arxiv_id":"2309.17257","repositories_listed":0,"syntology":null},{"url":null,"slug":"gsdc-transformer-an-efficient-and-effective","title":"GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation","date":"2023-09-29","arxiv_id":"2309.17059","repositories_listed":0,"syntology":null},{"url":null,"slug":"bevheight-toward-robust-visual-centric-3d","title":"BEVHeight++: Toward Robust Visual Centric 3D Object Detection","date":"2023-09-28","arxiv_id":"2309.16179","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-recurrent-lstm-and-transformer","title":"Gated Cross-Attention Network for Depth Completion","date":"2023-09-28","arxiv_id":"2309.16301","repositories_listed":0,"syntology":null},{"url":null,"slug":"photonic-accelerators-for-image-segmentation","title":"Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection","date":"2023-09-28","arxiv_id":"2309.16783","repositories_listed":0,"syntology":null},{"url":null,"slug":"superpixel-transformers-for-efficient","title":"Superpixel Transformers for Efficient Semantic Segmentation","date":"2023-09-28","arxiv_id":"2309.16889","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-decision-transformer-for","title":"Uncertainty-Aware Decision Transformer for Stochastic Driving Environments","date":"2023-09-28","arxiv_id":"2309.16397","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-multiple-object-tracking-on-autonomous","title":"3D Multiple Object Tracking on Autonomous Driving: A Literature Review","date":"2023-09-27","arxiv_id":"2309.15411","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoding-tree-for-city-generation-and","title":"AutoEncoding Tree for City Generation and Applications","date":"2023-09-27","arxiv_id":"2309.15941","repositories_listed":0,"syntology":null},{"url":null,"slug":"hpl-vit-a-unified-perception-framework-for","title":"HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V","date":"2023-09-27","arxiv_id":"2309.15572","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-imitation-learning-from-black-box-to","title":"Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies","date":"2023-09-27","arxiv_id":"2309.16025","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-robust-semantic-segmentation-uncv2023","title":"The Robust Semantic Segmentation UNCV2023 Challenge Results","date":"2023-09-27","arxiv_id":"2309.15478","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-via-neural","title":"Uncertainty Quantification via Neural Posterior Principal Components","date":"2023-09-27","arxiv_id":"2309.15533","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-data-misalignment-in-image-lidar","title":"Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation","date":"2023-09-26","arxiv_id":"2309.14932","repositories_listed":0,"syntology":null},{"url":null,"slug":"drivescenegen-generating-diverse-and","title":"DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch","date":"2023-09-26","arxiv_id":"2309.14685","repositories_listed":0,"syntology":null},{"url":null,"slug":"v2x-lead-lidar-based-end-to-end-autonomous","title":"V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration","date":"2023-09-26","arxiv_id":"2309.15252","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-on-video-object","title":"Adversarial Attacks on Video Object Segmentation with Hard Region Discovery","date":"2023-09-25","arxiv_id":"2309.13857","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-3d-perception-with-2d-vision","title":"Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving","date":"2023-09-25","arxiv_id":"2309.14491","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-offline-reinforcement-learning-for","title":"Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills","date":"2023-09-24","arxiv_id":"2309.13614","repositories_listed":0,"syntology":null},{"url":null,"slug":"computer-vision-technology-for-robotized-wire","title":"Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities","date":"2023-09-24","arxiv_id":"2309.13745","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-connector-detection-for","title":"Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses","date":"2023-09-24","arxiv_id":"2309.13746","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-aware-autonomous-driving-a-case-study","title":"Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios","date":"2023-09-22","arxiv_id":"2309.13206","repositories_listed":0,"syntology":null},{"url":null,"slug":"output-sampled-model-predictive-path-integral","title":"Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency","date":"2023-09-22","arxiv_id":"2309.13201","repositories_listed":0,"syntology":null},{"url":null,"slug":"electric-autonomous-mobility-on-demand","title":"Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation","date":"2023-09-21","arxiv_id":"2309.13012","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-drive-anywhere","title":"Learning to Drive Anywhere","date":"2023-09-21","arxiv_id":"2309.12295","repositories_listed":0,"syntology":null},{"url":null,"slug":"ppd-a-new-valet-parking-pedestrian-fisheye","title":"PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving","date":"2023-09-20","arxiv_id":"2309.11002","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-can-have-your-ensemble-and-run-it-too","title":"You can have your ensemble and run it too -- Deep Ensembles Spread Over Time","date":"2023-09-20","arxiv_id":"2309.11333","repositories_listed":0,"syntology":null},{"url":null,"slug":"drive-as-you-speak-enabling-human-like","title":"Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles","date":"2023-09-19","arxiv_id":"2309.10228","repositories_listed":0,"syntology":null},{"url":null,"slug":"linemarknet-line-landmark-detection-for-valet","title":"LineMarkNet: Line Landmark Detection for Valet Parking","date":"2023-09-19","arxiv_id":"2309.10475","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-pomdp-online-planning-via-shielding","title":"Safe POMDP Online Planning via Shielding","date":"2023-09-19","arxiv_id":"2309.10216","repositories_listed":0,"syntology":null},{"url":null,"slug":"cc-sgg-corner-case-scenario-generation-using","title":"CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs","date":"2023-09-18","arxiv_id":"2309.09844","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditioning-latent-space-clusters-for-real","title":"Conditioning Latent-Space Clusters for Real-World Anomaly Classification","date":"2023-09-18","arxiv_id":"2309.09676","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-online-distillation-promoting-safe","title":"Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration","date":"2023-09-18","arxiv_id":"2309.09408","repositories_listed":0,"syntology":null},{"url":null,"slug":"privileged-to-predicted-towards-sensorimotor","title":"Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving","date":"2023-09-18","arxiv_id":"2309.09756","repositories_listed":0,"syntology":null},{"url":null,"slug":"specification-driven-video-search-via","title":"Specification-Driven Video Search via Foundation Models and Formal Verification","date":"2023-09-18","arxiv_id":"2309.10171","repositories_listed":0,"syntology":null},{"url":null,"slug":"kinematics-aware-trajectory-generation-and","title":"Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling","date":"2023-09-17","arxiv_id":"2309.09317","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-machines-work-in-unstructured","title":"Intelligent machines work in unstructured environments by differential neuromorphic computing","date":"2023-09-16","arxiv_id":"2309.08835","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-camera-bird-s-eye-view-perception-for","title":"Multi-camera Bird's Eye View Perception for Autonomous Driving","date":"2023-09-16","arxiv_id":"2309.09080","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-communications-in-collaborative","title":"Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving","date":"2023-09-15","arxiv_id":"2310.00013","repositories_listed":0,"syntology":null},{"url":null,"slug":"occupancydetr-making-semantic-scene","title":"OccupancyDETR: Using DETR for Mixed Dense-sparse 3D Occupancy Prediction","date":"2023-09-15","arxiv_id":"2309.08504","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-frame-to-frame-camera-rotation","title":"Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes","date":"2023-09-15","arxiv_id":"2309.08588","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-different-backbone-architecture","title":"The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset","date":"2023-09-15","arxiv_id":"2309.08564","repositories_listed":0,"syntology":null},{"url":"/paper/tfnet-exploiting-temporal-cues-for-fast-and","slug":"tfnet-exploiting-temporal-cues-for-fast-and","title":"TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation","date":"2023-09-14","arxiv_id":"2309.07849","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-matters-to-enhance-traffic-rule","title":"What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving","date":"2023-09-14","arxiv_id":"2309.07808","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformer-dct-driven-enhancement-transformer","title":"DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision","date":"2023-09-13","arxiv_id":"2309.06941","repositories_listed":0,"syntology":null},{"url":null,"slug":"amodalsynthdrive-a-synthetic-amodal","title":"AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving","date":"2023-09-12","arxiv_id":"2309.06547","repositories_listed":0,"syntology":null},{"url":null,"slug":"emergent-communication-in-multi-agent","title":"Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks","date":"2023-09-12","arxiv_id":"2309.06021","repositories_listed":0,"syntology":null},{"url":null,"slug":"scp-scene-completion-pre-training-for-3d","title":"SCP: Scene Completion Pre-training for 3D Object Detection","date":"2023-09-12","arxiv_id":"2309.06199","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-you-text-what-is-happening-integrating","title":"Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving","date":"2023-09-11","arxiv_id":"2309.05282","repositories_listed":0,"syntology":null},{"url":null,"slug":"designs-and-implementations-in-neural-network","title":"Designs and Implementations in Neural Network-based Video Coding","date":"2023-09-11","arxiv_id":"2309.05846","repositories_listed":0,"syntology":null},{"url":null,"slug":"eanet-expert-attention-network-for-online","title":"EANet: Expert Attention Network for Online Trajectory Prediction","date":"2023-09-11","arxiv_id":"2309.05683","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusionformer-a-multi-sensory-fusion-in-bird-s","title":"FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection","date":"2023-09-11","arxiv_id":"2309.05257","repositories_listed":0,"syntology":null},{"url":null,"slug":"hilm-d-towards-high-resolution-understanding","title":"HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving","date":"2023-09-11","arxiv_id":"2309.05186","repositories_listed":0,"syntology":null},{"url":null,"slug":"shift3d-synthesizing-hard-inputs-for-tricking","title":"SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors","date":"2023-09-11","arxiv_id":"2309.05810","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-the-false-positive-rate-using","title":"Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception","date":"2023-09-09","arxiv_id":"2310.05951","repositories_listed":0,"syntology":null},{"url":null,"slug":"timely-fusion-of-surround-radar-lidar-for","title":"Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems","date":"2023-09-09","arxiv_id":"2309.04806","repositories_listed":0,"syntology":null},{"url":null,"slug":"mask2anomaly-mask-transformer-for-universal","title":"Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation","date":"2023-09-08","arxiv_id":"2309.04573","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-task-decathlon-unifying-image-and-video","title":"Video Task Decathlon: Unifying Image and Video Tasks in Autonomous Driving","date":"2023-09-08","arxiv_id":"2309.04422","repositories_listed":0,"syntology":null},{"url":null,"slug":"pbp-path-based-trajectory-prediction-for","title":"PBP: Path-based Trajectory Prediction for Autonomous Driving","date":"2023-09-07","arxiv_id":"2309.03750","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-neural-control-for-non-affine-control","title":"Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions","date":"2023-09-06","arxiv_id":"2309.04492","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-imitation-learning-algorithms","title":"A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges","date":"2023-09-05","arxiv_id":"2309.02473","repositories_listed":0,"syntology":null}],"record_sha256":"e37f2ec236a94c583b55302d671f9554d7c67c9a31159b33a4f7767246037634","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}