{"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/55","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":55,"pages_in_order":61,"rows_per_page":100,"rows":[5401,5500],"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/54","next":"/task/autonomous-driving/papers/56","papers":[{"url":null,"slug":"hidden-footprints-learning-contextual","title":"Hidden Footprints: Learning Contextual Walkability from 3D Human Trails","date":"2020-08-19","arxiv_id":"2008.08701","repositories_listed":0,"syntology":null},{"url":null,"slug":"ab3dmot-a-baseline-for-3d-multi-object","title":"AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics","date":"2020-08-18","arxiv_id":"2008.08063","repositories_listed":0,"syntology":null},{"url":null,"slug":"soda-multi-object-tracking-with-soft-data","title":"SoDA: Multi-Object Tracking with Soft Data Association","date":"2020-08-18","arxiv_id":"2008.07725","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-network-assisted-real-time-object","title":"Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving","date":"2020-08-17","arxiv_id":"2008.07083","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-stylization-for-robust-features","title":"Image Stylization for Robust Features","date":"2020-08-16","arxiv_id":"2008.06959","repositories_listed":0,"syntology":null},{"url":null,"slug":"instancemotseg-real-time-instance-motion","title":"Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline","date":"2020-08-16","arxiv_id":"2008.07008","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-aware-network-for-lane-marker","title":"Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor","date":"2020-08-14","arxiv_id":"2008.06204","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-localization-for-autonomous-driving","title":"Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze","date":"2020-08-13","arxiv_id":"2008.05678","repositories_listed":0,"syntology":null},{"url":null,"slug":"balanced-depth-completion-between-dense-depth","title":"Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP","date":"2020-08-12","arxiv_id":"2008.05158","repositories_listed":0,"syntology":null},{"url":"/paper/dawn-vehicle-detection-in-adverse-weather","slug":"dawn-vehicle-detection-in-adverse-weather","title":"DAWN: Vehicle Detection in Adverse Weather Nature Dataset","date":"2020-08-12","arxiv_id":"2008.05402","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforced-wasserstein-training-for-severity","title":"Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving","date":"2020-08-11","arxiv_id":"2008.04751","repositories_listed":0,"syntology":null},{"url":null,"slug":"labels-are-not-perfect-improving","title":"Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty","date":"2020-08-10","arxiv_id":"2008.04168","repositories_listed":0,"syntology":null},{"url":null,"slug":"measures-of-complexity-for-large-scale-image","title":"Measures of Complexity for Large Scale Image Datasets","date":"2020-08-10","arxiv_id":"2008.04431","repositories_listed":0,"syntology":null},{"url":null,"slug":"syndistnet-self-supervised-monocular-fisheye","title":"SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving","date":"2020-08-10","arxiv_id":"2008.04017","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidar-data-enrichment-using-deep-learning","title":"LiDAR Data Enrichment Using Deep Learning Based on High-Resolution Image: An Approach to Achieve High-Performance LiDAR SLAM Using Low-cost LiDAR","date":"2020-08-09","arxiv_id":"2008.03694","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sensitivity-analysis-approach-for","title":"A Sensitivity Analysis Approach for Evaluating a Radar Simulation for Virtual Testing of Autonomous Driving Functions","date":"2020-08-06","arxiv_id":"2008.02725","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-perception-using-light-curtains-for","title":"Active Perception using Light Curtains for Autonomous Driving","date":"2020-08-05","arxiv_id":"2008.02191","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-signal-processing-for-geometric-data","title":"Graph Signal Processing for Geometric Data and Beyond: Theory and Applications","date":"2020-08-05","arxiv_id":"2008.01918","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-deep-reinforcement","title":"A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles","date":"2020-08-04","arxiv_id":"2008.01302","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-source-coding-techniques-for","title":"Comparison of Source Coding Techniques for the Vehicle to Vehicle Communication","date":"2020-08-04","arxiv_id":"2008.02097","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-birds-eye-view-flow-estimation-for","title":"PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving","date":"2020-08-03","arxiv_id":"2008.01179","repositories_listed":0,"syntology":null},{"url":null,"slug":"iot-system-for-real-time-near-crash-detection","title":"Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications","date":"2020-08-02","arxiv_id":"2008.00549","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-material-recognition-in-light-fields-via","title":"Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distance-normalized-unified-representation","title":"Distance-Normalized Unified Representation for Monocular 3D Object Detection","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"levelset-r-cnn-a-deep-variational-method-for-1","title":"LevelSet R-CNN: A Deep Variational Method for Instance Segmentation","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-the-effects-of-windshield-refraction","title":"Modeling the Effects of Windshield Refraction for Camera Calibration","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"panonet-real-time-panoptic-segmentation","title":"PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding","date":"2020-08-01","arxiv_id":"2008.00192","repositories_listed":0,"syntology":null},{"url":null,"slug":"polynomial-regression-network-for-variable","title":"Polynomial Regression Network for Variable-Number Lane Detection","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"s3net-semantic-aware-self-supervised-depth","title":"S³Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-rank-for-active-learning-a","title":"Learning to Rank for Active Learning: A Listwise Approach","date":"2020-07-31","arxiv_id":"2008.00078","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-traffic-sign-detection-and-recognition","title":"Deep Traffic Sign Detection and Recognition Without Target Domain Real Images","date":"2020-07-30","arxiv_id":"2008.00962","repositories_listed":0,"syntology":null},{"url":null,"slug":"heatmap-based-vanishing-point-boosts-lane","title":"Heatmap-based Vanishing Point boosts Lane Detection","date":"2020-07-30","arxiv_id":"2007.15602","repositories_listed":0,"syntology":null},{"url":null,"slug":"levelset-r-cnn-a-deep-variational-method-for","title":"LevelSet R-CNN: A Deep Variational Method for Instance Segmentation","date":"2020-07-30","arxiv_id":"2007.15629","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneously-learning-corrections-and-error","title":"Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods","date":"2020-07-29","arxiv_id":"2007.14943","repositories_listed":0,"syntology":null},{"url":null,"slug":"speed-up-heuristic-for-an-on-demand-ride","title":"Speed-up Heuristic for an On-Demand Ride-Pooling Algorithm","date":"2020-07-29","arxiv_id":"2007.14877","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-framework-for-generation-and","title":"A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories","date":"2020-07-28","arxiv_id":"2007.14524","repositories_listed":0,"syntology":null},{"url":null,"slug":"s-3-net-semantic-aware-self-supervised-depth","title":"$S^3$Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data","date":"2020-07-28","arxiv_id":"2007.14511","repositories_listed":0,"syntology":null},{"url":null,"slug":"yolopeds-efficient-real-time-single-shot","title":"YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications","date":"2020-07-27","arxiv_id":"2007.13404","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-semantic-segmentation-to-autonomous","title":"Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment","date":"2020-07-25","arxiv_id":"2007.12869","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-lstm-approach-to-temporal-3d-object","title":"An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds","date":"2020-07-24","arxiv_id":"2007.12392","repositories_listed":0,"syntology":null},{"url":null,"slug":"grid-based-stochastic-model-predictive","title":"Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments","date":"2020-07-24","arxiv_id":"2007.12430","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-transfer-learning-for-autonomous","title":"Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation","date":"2020-07-23","arxiv_id":"2007.12148","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-robustness-verification-on","title":"Scaling Polyhedral Neural Network Verification on GPUs","date":"2020-07-20","arxiv_id":"2007.10868","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-robustness-verification-of-deep","title":"Accelerating Robustness Verification of Deep Neural Networks Guided by Target Labels","date":"2020-07-16","arxiv_id":"2007.08520","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-making-strategy-on-highway-for","title":"Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning","date":"2020-07-16","arxiv_id":"2007.08691","repositories_listed":0,"syntology":null},{"url":"/paper/infofocus-3d-object-detection-for-autonomous","slug":"infofocus-3d-object-detection-for-autonomous","title":"InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling","date":"2020-07-16","arxiv_id":"2007.08556","repositories_listed":0,"syntology":null},{"url":null,"slug":"permo-perceiving-more-at-once-from-a-single","title":"PerMO: Perceiving More at Once from a Single Image for Autonomous Driving","date":"2020-07-16","arxiv_id":"2007.08116","repositories_listed":0,"syntology":null},{"url":null,"slug":"skyscapes-fine-grained-semantic-understanding-1","title":"SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes","date":"2020-07-12","arxiv_id":"2007.06102","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quick-review-on-recent-trends-in-3d-point","title":"A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain","date":"2020-07-08","arxiv_id":"2007.05038","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformable-spatial-propagation-network-for","title":"Deformable spatial propagation network for depth completion","date":"2020-07-08","arxiv_id":"2007.04251","repositories_listed":0,"syntology":null},{"url":null,"slug":"enable-an-open-software-defined-mobility","title":"Enable an Open Software Defined Mobility Ecosystem through VEC-OF","date":"2020-07-08","arxiv_id":"2007.03879","repositories_listed":0,"syntology":null},{"url":null,"slug":"kit-moma-a-mobile-machines-dataset","title":"KIT MOMA: A Mobile Machines Dataset","date":"2020-07-08","arxiv_id":"2007.04198","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-to-real-domain-adaptation-for-lane","title":"Synthetic-to-Real Domain Adaptation for Lane Detection","date":"2020-07-08","arxiv_id":"2007.04023","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-agent-trajectory-prediction-using","title":"Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism","date":"2020-07-06","arxiv_id":"2007.02515","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-with-koopman-operators","title":"Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles","date":"2020-07-05","arxiv_id":"2007.02219","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-defenses-against-adversarial","title":"Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment","date":"2020-07-02","arxiv_id":"2007.01017","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-for-computational","title":"Deep Neural Networks for Computational Optical Form Measurements","date":"2020-07-01","arxiv_id":"2007.00319","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-prediction-in-visual-object-tracking","title":"Motion Prediction in Visual Object Tracking","date":"2020-07-01","arxiv_id":"2007.01120","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-navigation-with-tinyml-for-autonomous","title":"Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles","date":"2020-07-01","arxiv_id":"2007.00302","repositories_listed":0,"syntology":null},{"url":null,"slug":"tiledsoilingnet-tile-level-soiling-detection","title":"TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric","date":"2020-07-01","arxiv_id":"2007.00801","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-reinforcement-learning-agent","title":"Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback","date":"2020-06-30","arxiv_id":"2006.16498","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-deep-neural-networks-with","title":"Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey","date":"2020-06-30","arxiv_id":"2006.16867","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-under-rainy-conditions-for","title":"Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques","date":"2020-06-30","arxiv_id":"2006.16471","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-lidar-based-perception-in","title":"Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures","date":"2020-06-30","arxiv_id":"2006.16974","repositories_listed":0,"syntology":null},{"url":null,"slug":"vehicle-re-id-for-surround-view-camera-system","title":"Vehicle Re-ID for Surround-view Camera System","date":"2020-06-30","arxiv_id":"2006.16503","repositories_listed":0,"syntology":null},{"url":null,"slug":"2nd-place-solution-for-waymo-open-dataset","title":"2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection","date":"2020-06-28","arxiv_id":"2006.15507","repositories_listed":0,"syntology":null},{"url":"/paper/localization-uncertainty-estimation-for","slug":"localization-uncertainty-estimation-for","title":"Localization Uncertainty Estimation for Anchor-Free Object Detection","date":"2020-06-28","arxiv_id":"2006.15607","repositories_listed":0,"syntology":null},{"url":null,"slug":"lessons-learned-from-accident-of-autonomous","title":"Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework","date":"2020-06-27","arxiv_id":"2006.15382","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-predictive-representations-in","title":"Learning predictive representations in autonomous driving to improve deep reinforcement learning","date":"2020-06-26","arxiv_id":"2006.15110","repositories_listed":0,"syntology":null},{"url":null,"slug":"atso-asynchronous-teacher-student","title":"ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation","date":"2020-06-24","arxiv_id":"2006.13461","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-term-prediction-of-lane-change-maneuver","title":"Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron","date":"2020-06-23","arxiv_id":"2006.12769","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-shot-3d-detection-of-vehicles-from","title":"Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time","date":"2020-06-23","arxiv_id":"2006.13084","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-sensor-fusion-in-visual","title":"Adversarial Robustness of Deep Sensor Fusion Models","date":"2020-06-23","arxiv_id":"2006.13192","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sampling-based-maximum-entropy","title":"Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving","date":"2020-06-22","arxiv_id":"2006.13704","repositories_listed":0,"syntology":null},{"url":null,"slug":"provably-efficient-causal-reinforcement","title":"Provably Efficient Causal Reinforcement Learning with Confounded Observational Data","date":"2020-06-22","arxiv_id":"2006.12311","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-lidar-point-cloud-interpolation-based","title":"Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision","date":"2020-06-20","arxiv_id":"2006.11481","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-of-agent-behavior-through","title":"Generalization of Agent Behavior through Explicit Representation of Context","date":"2020-06-18","arxiv_id":"2006.11305","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-with-uncertainty","title":"Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections","date":"2020-06-17","arxiv_id":"2006.09786","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-dad-net-binarized-driveable-area","title":"Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving","date":"2020-06-15","arxiv_id":"2006.08178","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-domain-mismatch-estimation","title":"Self-Supervised Domain Mismatch Estimation for Autonomous Perception","date":"2020-06-15","arxiv_id":"2006.08613","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-incorporating-contextual-knowledge","title":"Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior","date":"2020-06-15","arxiv_id":"2006.08470","repositories_listed":0,"syntology":null},{"url":null,"slug":"visibility-guided-nms-efficient-boosting-of","title":"Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes","date":"2020-06-15","arxiv_id":"2006.08547","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-prediction-architecture-for","title":"Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform","date":"2020-06-11","arxiv_id":"2006.06715","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-driving-with-deep-learning-a","title":"Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies","date":"2020-06-10","arxiv_id":"2006.06091","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-a-stochastic-traffic","title":"Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents","date":"2020-06-10","arxiv_id":"2006.05821","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-navigation-costs-from-demonstration-1","title":"Learning Navigation Costs from Demonstration with Semantic Observations","date":"2020-06-09","arxiv_id":"2006.05043","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvlidarnet-real-time-multi-class-scene","title":"MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views","date":"2020-06-09","arxiv_id":"2006.05518","repositories_listed":0,"syntology":null},{"url":null,"slug":"off-the-shelf-sensor-vs-experimental-radar","title":"Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?","date":"2020-06-09","arxiv_id":"2006.05485","repositories_listed":0,"syntology":null},{"url":null,"slug":"stereo-rgb-and-deeper-lidar-based-network-for","title":"Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection","date":"2020-06-09","arxiv_id":"2006.05187","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-we-hungry-for-3d-lidar-data-for-semantic","title":"Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study","date":"2020-06-08","arxiv_id":"2006.04307","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-navigation-costs-from-demonstrations","title":"Learning Navigation Costs from Demonstrations with Semantic Observations","date":"2020-06-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-semantic-mapping-for-urban","title":"Probabilistic Semantic Mapping for Urban Autonomous Driving Applications","date":"2020-06-08","arxiv_id":"2006.04894","repositories_listed":0,"syntology":null},{"url":null,"slug":"unstructured-road-vanishing-point-detection","title":"Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression","date":"2020-06-08","arxiv_id":"2006.04691","repositories_listed":0,"syntology":null},{"url":"/paper/svga-net-sparse-voxel-graph-attention-network","slug":"svga-net-sparse-voxel-graph-attention-network","title":"SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds","date":"2020-06-07","arxiv_id":"2006.04043","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-autonomous-driving-by-learning-end","title":"Explaining Autonomous Driving by Learning End-to-End Visual Attention","date":"2020-06-05","arxiv_id":"2006.03347","repositories_listed":0,"syntology":null},{"url":null,"slug":"causality-and-batch-reinforcement-learning","title":"Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains","date":"2020-06-03","arxiv_id":"2006.02579","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-vehicular-networks","title":"Federated Learning in Vehicular Networks","date":"2020-06-02","arxiv_id":"2006.01412","repositories_listed":0,"syntology":null},{"url":null,"slug":"resolving-class-imbalance-in-object-detection","title":"Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses","date":"2020-06-02","arxiv_id":"2006.01413","repositories_listed":0,"syntology":null},{"url":"/paper/a-survey-on-deep-learning-techniques-for","slug":"a-survey-on-deep-learning-techniques-for","title":"A Survey on Deep Learning Techniques for Stereo-based Depth Estimation","date":"2020-06-01","arxiv_id":"2006.02535","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-building-and-labeling-of-hd-maps","title":"Automatic Building and Labeling of HD Maps with Deep Learning","date":"2020-06-01","arxiv_id":"2006.00644","repositories_listed":0,"syntology":null}],"record_sha256":"b987c004577b9503955ce57d92564832b86c3519478f9f45e56ac4005bbc8614","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}