{"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/38","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":38,"pages_in_order":61,"rows_per_page":100,"rows":[3701,3800],"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/37","next":"/task/autonomous-driving/papers/39","papers":[{"url":null,"slug":"neurosymbolic-value-inspired-ai-why-what-and","title":"Neurosymbolic Value-Inspired AI (Why, What, and How)","date":"2023-12-15","arxiv_id":"2312.09928","repositories_listed":0,"syntology":null},{"url":null,"slug":"slowtrack-increasing-the-latency-of-camera","title":"SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples","date":"2023-12-15","arxiv_id":"2312.09520","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-aware-multi-camera-3d-object","title":"Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning","date":"2023-12-13","arxiv_id":"2312.08004","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-of-neural-networks-with-uncertain","title":"Training of Neural Networks with Uncertain Data: A Mixture of Experts Approach","date":"2023-12-13","arxiv_id":"2312.08083","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-driving-of-trucks-in-off-road","title":"Autonomous driving of trucks in off-road environment","date":"2023-12-12","arxiv_id":"2312.07382","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-large-language-models-for-1","title":"Evaluation of Large Language Models for Decision Making in Autonomous Driving","date":"2023-12-11","arxiv_id":"2312.06351","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-long-term-waypoint-based","title":"Interpretable Long Term Waypoint-Based Trajectory Prediction Model","date":"2023-12-11","arxiv_id":"2312.06219","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-deterministic-human-motion","title":"Recent Advances in Deterministic Human Motion Prediction: A Review","date":"2023-12-11","arxiv_id":"2312.06184","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-decentralized-cooperative-platoon","title":"Scalable Decentralized Cooperative Platoon using Multi-Agent Deep Reinforcement Learning","date":"2023-12-11","arxiv_id":"2312.06858","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-event-graphs-for-dynamic-scene","title":"Spatiotemporal Event Graphs for Dynamic Scene Understanding","date":"2023-12-11","arxiv_id":"2312.07621","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-one-model-fits-all-ensemble-deep","title":"Beyond One Model Fits All: Ensemble Deep Learning for Autonomous Vehicles","date":"2023-12-10","arxiv_id":"2312.05759","repositories_listed":0,"syntology":null},{"url":null,"slug":"gendepth-generalizing-monocular-depth","title":"GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding","date":"2023-12-10","arxiv_id":"2312.06021","repositories_listed":0,"syntology":null},{"url":null,"slug":"prospective-role-of-foundation-models-in","title":"Prospective Role of Foundation Models in Advancing Autonomous Vehicles","date":"2023-12-08","arxiv_id":"2405.02288","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-dynamics-vehicle-dynamics-modeling-with","title":"Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing","date":"2023-12-07","arxiv_id":"2312.04374","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-driven-simulation-benchmark","title":"Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes","date":"2023-12-07","arxiv_id":"2312.04008","repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-graph-convolutional-network-for-bird","title":"Residual Graph Convolutional Network for Bird's-Eye-View Semantic Segmentation","date":"2023-12-07","arxiv_id":"2312.04044","repositories_listed":0,"syntology":null},{"url":"/paper/dginstyle-domain-generalizable-semantic","slug":"dginstyle-domain-generalizable-semantic","title":"DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control","date":"2023-12-05","arxiv_id":"2312.03048","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-insights-towards-explainable-and","title":"Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction","date":"2023-12-05","arxiv_id":"2312.02872","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgtr-multi-granular-transformer-for-motion","title":"MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR","date":"2023-12-05","arxiv_id":"2312.02409","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyze-drivers-intervention-behavior-during","title":"Analyze Drivers' Intervention Behavior During Autonomous Driving -- A VR-incorporated Approach","date":"2023-12-04","arxiv_id":"2312.01669","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-adversarial-robustness-of-lidar","title":"Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving","date":"2023-12-03","arxiv_id":"2312.01468","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-efficiency-of-dnn-based","title":"Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing","date":"2023-12-01","arxiv_id":"2312.00316","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-3d-object-detection-in-bird","title":"Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach","date":"2023-12-01","arxiv_id":"2312.00633","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-deep-reinforcement-learning-2","title":"Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control","date":"2023-11-30","arxiv_id":"2311.18393","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-based-trajectory","title":"Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions","date":"2023-11-30","arxiv_id":"2311.18553","repositories_listed":0,"syntology":null},{"url":null,"slug":"erasing-the-ephemeral-joint-camera-refinement","title":"Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis","date":"2023-11-29","arxiv_id":"2311.17634","repositories_listed":0,"syntology":null},{"url":null,"slug":"game-projection-and-robustness-for-game","title":"Game Projection and Robustness for Game-Theoretic Autonomous Driving","date":"2023-11-29","arxiv_id":"2311.18074","repositories_listed":0,"syntology":null},{"url":null,"slug":"depthssc-depth-spatial-alignment-and-dynamic","title":"DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation","date":"2023-11-28","arxiv_id":"2311.17084","repositories_listed":0,"syntology":null},{"url":null,"slug":"dgnr-density-guided-neural-point-rendering-of","title":"DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes","date":"2023-11-28","arxiv_id":"2311.16664","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-autonomous-driving-with-large","title":"Empowering Autonomous Driving with Large Language Models: A Safety Perspective","date":"2023-11-28","arxiv_id":"2312.00812","repositories_listed":0,"syntology":null},{"url":null,"slug":"lane-keeping-control-of-autonomous-vehicles","title":"Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR","date":"2023-11-28","arxiv_id":"2311.16900","repositories_listed":0,"syntology":null},{"url":null,"slug":"uc-nerf-neural-radiance-field-for-under","title":"UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving","date":"2023-11-28","arxiv_id":"2311.16945","repositories_listed":0,"syntology":null},{"url":null,"slug":"soac-spatio-temporal-overlap-aware-multi","title":"SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields","date":"2023-11-27","arxiv_id":"2311.15803","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-pedestrian-character-learning-for","title":"Sparse Pedestrian Character Learning for Trajectory Prediction","date":"2023-11-27","arxiv_id":"2311.15512","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-for-argoverse-challenges-on","title":"Technical Report for Argoverse Challenges on 4D Occupancy Forecasting","date":"2023-11-27","arxiv_id":"2311.15660","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-for-argoverse-challenges-on-1","title":"Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecasting","date":"2023-11-27","arxiv_id":"2311.15615","repositories_listed":0,"syntology":null},{"url":null,"slug":"calibformer-a-transformer-based-automatic","title":"CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network","date":"2023-11-26","arxiv_id":"2311.15241","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-based-lidar-intensity-simulation","title":"GAN-Based LiDAR Intensity Simulation","date":"2023-11-26","arxiv_id":"2311.15415","repositories_listed":0,"syntology":null},{"url":null,"slug":"gbd-ts-goal-based-pedestrian-trajectory","title":"GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction","date":"2023-11-25","arxiv_id":"2311.14922","repositories_listed":0,"syntology":null},{"url":null,"slug":"opennet-incremental-learning-for-autonomous","title":"OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss","date":"2023-11-25","arxiv_id":"2311.14939","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-4v-takes-the-wheel-evaluating-promise-and","title":"GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction","date":"2023-11-24","arxiv_id":"2311.14786","repositories_listed":0,"syntology":null},{"url":null,"slug":"security-and-privacy-challenges-in-deep","title":"Security and Privacy Challenges in Deep Learning Models","date":"2023-11-23","arxiv_id":"2311.13744","repositories_listed":0,"syntology":null},{"url":null,"slug":"adriver-i-a-general-world-model-for","title":"ADriver-I: A General World Model for Autonomous Driving","date":"2023-11-22","arxiv_id":"2311.13549","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-uncertainty-estimation","title":"An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams","date":"2023-11-22","arxiv_id":"2311.13374","repositories_listed":0,"syntology":null},{"url":null,"slug":"doubleaug-single-domain-generalized-object","title":"DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory","date":"2023-11-22","arxiv_id":"2311.13198","repositories_listed":0,"syntology":null},{"url":null,"slug":"attacking-motion-planners-using-adversarial","title":"Attacking Motion Planners Using Adversarial Perception Errors","date":"2023-11-21","arxiv_id":"2311.12722","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-chemical-versus-electrical","title":"Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?","date":"2023-11-21","arxiv_id":"2401.08602","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-scale-car-parts-lscp-dataset-for","title":"A Large-Scale Car Parts (LSCP) Dataset for Lightweight Fine-Grained Detection","date":"2023-11-20","arxiv_id":"2311.11754","repositories_listed":0,"syntology":null},{"url":null,"slug":"applications-of-large-scale-foundation-models","title":"Applications of Large Scale Foundation Models for Autonomous Driving","date":"2023-11-20","arxiv_id":"2311.12144","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-boundaries-a-comprehensive-survey-of","title":"Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems","date":"2023-11-20","arxiv_id":"2311.11796","repositories_listed":0,"syntology":null},{"url":null,"slug":"vip-mixer-a-convolutional-mixer-for-video","title":"SIAM: A Simple Alternating Mixer for Video Prediction","date":"2023-11-20","arxiv_id":"2311.11683","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-timescale-control-and-communications","title":"Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control","date":"2023-11-19","arxiv_id":"2311.11281","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-simulators-for-autonomous-driving","title":"Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving","date":"2023-11-18","arxiv_id":"2311.11056","repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-perception-with-learning-based","title":"Cooperative Perception with Learning-Based V2V communications","date":"2023-11-17","arxiv_id":"2311.10336","repositories_listed":0,"syntology":null},{"url":null,"slug":"imagination-augmented-hierarchical","title":"Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments","date":"2023-11-17","arxiv_id":"2311.10309","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-meets-mmwave-radar-3d-object","title":"Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving","date":"2023-11-17","arxiv_id":"2311.10261","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-generation-of-scenarios-for-system","title":"Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems","date":"2023-11-16","arxiv_id":"2311.09784","repositories_listed":0,"syntology":null},{"url":null,"slug":"applications-of-computer-vision-in-autonomous","title":"Why Autonomous Vehicles Are Not Ready Yet: A Multi-Disciplinary Review of Problems, Attempted Solutions, and Future Directions","date":"2023-11-15","arxiv_id":"2311.09093","repositories_listed":0,"syntology":null},{"url":null,"slug":"lateral-control-for-autonomous-vehicles-a","title":"Lateral control for autonomous vehicles: A comparative evaluation","date":"2023-11-14","arxiv_id":"2311.07987","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-light-pedestrian-detection-in-visible-and","title":"Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges","date":"2023-11-14","arxiv_id":"2311.08557","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-improving-robustness-against-common","title":"Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning","date":"2023-11-14","arxiv_id":"2311.07928","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-performance-prediction-for-deep","title":"Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks","date":"2023-11-13","arxiv_id":"2311.07477","repositories_listed":0,"syntology":null},{"url":null,"slug":"vt-former-a-transformer-based-vehicle","title":"VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer","date":"2023-11-11","arxiv_id":"2311.06623","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-3d-object-detection-and","title":"Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey","date":"2023-11-10","arxiv_id":"2311.06043","repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-the-once-benchmark-with","title":"Refining the ONCE Benchmark with Hyperparameter Tuning","date":"2023-11-10","arxiv_id":"2311.06054","repositories_listed":0,"syntology":null},{"url":null,"slug":"ffinet-future-feedback-interaction-network","title":"FFINet: Future Feedback Interaction Network for Motion Forecasting","date":"2023-11-08","arxiv_id":"2311.04512","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-patch-matching-with-graph-based","title":"Image Patch-Matching with Graph-Based Learning in Street Scenes","date":"2023-11-08","arxiv_id":"2311.04617","repositories_listed":0,"syntology":null},{"url":null,"slug":"pred-pre-training-via-semantic-rendering-on","title":"PRED: Pre-training via Semantic Rendering on LiDAR Point Clouds","date":"2023-11-08","arxiv_id":"2311.04501","repositories_listed":0,"syntology":null},{"url":null,"slug":"agnes-abstraction-guided-framework-for-deep","title":"AGNES: Abstraction-guided Framework for Deep Neural Networks Security","date":"2023-11-07","arxiv_id":"2311.04009","repositories_listed":0,"syntology":null},{"url":null,"slug":"cola-coarse-label-multi-source-lidar-semantic","title":"COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving","date":"2023-11-06","arxiv_id":"2311.03017","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexible-multi-generator-model-with-fused","title":"Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction","date":"2023-11-06","arxiv_id":"2311.02835","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-3d-point-cloud","title":"Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook","date":"2023-11-05","arxiv_id":"2311.02608","repositories_listed":0,"syntology":null},{"url":null,"slug":"levels-of-agi-operationalizing-progress-on","title":"Levels of AGI for Operationalizing Progress on the Path to AGI","date":"2023-11-04","arxiv_id":"2311.02462","repositories_listed":0,"syntology":null},{"url":null,"slug":"osm-vs-hd-maps-map-representations-for","title":"OSM vs HD Maps: Map Representations for Trajectory Prediction","date":"2023-11-04","arxiv_id":"2311.02305","repositories_listed":0,"syntology":null},{"url":null,"slug":"p2o-calib-camera-lidar-calibration-using","title":"P2O-Calib: Camera-LiDAR Calibration Using Point-Pair Spatial Occlusion Relationship","date":"2023-11-04","arxiv_id":"2311.02413","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-of-deep-learning","title":"Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities","date":"2023-11-04","arxiv_id":"2311.02485","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-lidar-localization-and-mapping-pipeline","title":"Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving","date":"2023-11-03","arxiv_id":"2311.01823","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantitative-evaluation-of-a-multi-modal","title":"Quantitative Evaluation of a Multi-Modal Camera Setup for Fusing Event Data with RGB Images","date":"2023-11-03","arxiv_id":"2311.01881","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversary-ml-resilience-in-autonomous-driving","title":"Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms","date":"2023-11-02","arxiv_id":"2311.01478","repositories_listed":0,"syntology":null},{"url":null,"slug":"centerradarnet-joint-3d-object-detection-and","title":"CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar","date":"2023-11-02","arxiv_id":"2311.01423","repositories_listed":0,"syntology":null},{"url":null,"slug":"cml-mots-collaborative-multi-task-learning","title":"CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation","date":"2023-11-02","arxiv_id":"2311.00987","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-policy-learning-for-sensorimotor","title":"Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts","date":"2023-11-02","arxiv_id":"2311.01457","repositories_listed":0,"syntology":null},{"url":null,"slug":"drnet-a-decision-making-method-for-autonomous","title":"DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning","date":"2023-11-02","arxiv_id":"2311.01602","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-vision-transformer-for-accurate","title":"Efficient Vision Transformer for Accurate Traffic Sign Detection","date":"2023-11-02","arxiv_id":"2311.01429","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-unsupervised-world-models-for","title":"Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion","date":"2023-11-02","arxiv_id":"2311.01017","repositories_listed":0,"syntology":null},{"url":null,"slug":"revealing-cnn-architectures-via-side-channel","title":"Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based Inference Accelerators","date":"2023-11-01","arxiv_id":"2311.00579","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-limitations-of-state-aware","title":"Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving","date":"2023-10-31","arxiv_id":"2310.20650","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-the-spatial-awareness-capability-of","title":"Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model","date":"2023-10-31","arxiv_id":"2310.20357","repositories_listed":0,"syntology":null},{"url":null,"slug":"flodcast-flow-and-depth-forecasting-via","title":"FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures","date":"2023-10-31","arxiv_id":"2310.20593","repositories_listed":0,"syntology":null},{"url":null,"slug":"safety-aware-causal-representation-for","title":"Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving","date":"2023-10-31","arxiv_id":"2311.10747","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-language-models-using-formal","title":"Fine-Tuning Language Models Using Formal Methods Feedback","date":"2023-10-27","arxiv_id":"2310.18239","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-data-augmentation-for-offline","title":"Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning","date":"2023-10-27","arxiv_id":"2310.18247","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-graph-network-for-complex-activity","title":"A Hybrid Graph Network for Complex Activity Detection in Video","date":"2023-10-26","arxiv_id":"2310.17493","repositories_listed":0,"syntology":null},{"url":null,"slug":"drive-anywhere-generalizable-end-to-end","title":"Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models","date":"2023-10-26","arxiv_id":"2310.17642","repositories_listed":0,"syntology":null},{"url":null,"slug":"eqdrive-efficient-equivariant-motion","title":"EqDrive: Efficient Equivariant Motion Forecasting with Multi-Modality for Autonomous Driving","date":"2023-10-26","arxiv_id":"2310.17540","repositories_listed":0,"syntology":null},{"url":null,"slug":"yolo-bev-generating-bird-s-eye-view-in-the","title":"YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection","date":"2023-10-26","arxiv_id":"2310.17379","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvfan-multi-view-feature-assisted-network-for","title":"MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection","date":"2023-10-25","arxiv_id":"2310.16389","repositories_listed":0,"syntology":null},{"url":null,"slug":"parisluco3d-a-high-quality-target-dataset-for","title":"ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception","date":"2023-10-25","arxiv_id":"2310.16542","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-knowledge-awareness-to-improve-safety","title":"Using Knowledge Awareness to improve Safety of Autonomous Driving","date":"2023-10-25","arxiv_id":"2310.16760","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-traffic-simulation-a","title":"Data-driven Traffic Simulation: A Comprehensive Review","date":"2023-10-24","arxiv_id":"2310.15975","repositories_listed":0,"syntology":null}],"record_sha256":"7945bf9aabce32d216e51dccd51bea0f622810e98e942957695392d37ebd34f0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}