{"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-vehicles/papers/6","list_of":"/task/autonomous-vehicles","task":"Autonomous Vehicles","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":6,"pages_in_order":27,"rows_per_page":100,"rows":[501,600],"of":2605,"counts":{"archive_papers_tagged":2605,"with_a_code_link":695,"where_syntology_ran_a_sample":113,"not_listed_spam_title":0,"listed":2605,"listed_where_code_ran":113,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":93,"every_run_a_failure_of_syntologys_instrument":20,"listed_with_a_run_with_no_instrument_failure":93,"listed_every_run_a_failure_of_syntologys_instrument":20,"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-vehicles","prev":"/task/autonomous-vehicles/papers/5","next":"/task/autonomous-vehicles/papers/7","papers":[{"url":"/paper/a-framework-for-multisensory-foresight-for","slug":"a-framework-for-multisensory-foresight-for","title":"A Framework for Multisensory Foresight for Embodied Agents","date":"2021-09-15","arxiv_id":"2109.07561","repositories_listed":1,"syntology":null},{"url":"/paper/dsor-a-scalable-statistical-filter-for","slug":"dsor-a-scalable-statistical-filter-for","title":"DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather","date":"2021-09-15","arxiv_id":"2109.07078","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-navigate-intersections-with","slug":"learning-to-navigate-intersections-with","title":"Learning to Navigate Intersections with Unsupervised Driver Trait Inference","date":"2021-09-14","arxiv_id":"2109.06783","repositories_listed":1,"syntology":null},{"url":"/paper/neural-network-guided-evolutionary-fuzzing","slug":"neural-network-guided-evolutionary-fuzzing","title":"Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles","date":"2021-09-13","arxiv_id":"2109.06126","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-multi-modal-motion-planning-with","slug":"interactive-multi-modal-motion-planning-with","title":"Interactive multi-modal motion planning with Branch Model Predictive Control","date":"2021-09-10","arxiv_id":"2109.05128","repositories_listed":1,"syntology":null},{"url":"/paper/multi-agent-variational-occlusion-inference","slug":"multi-agent-variational-occlusion-inference","title":"Multi-Agent Variational Occlusion Inference Using People as Sensors","date":"2021-09-05","arxiv_id":"2109.02173","repositories_listed":1,"syntology":null},{"url":"/paper/optimized-self-adaptive-pid-speed-control-for","slug":"optimized-self-adaptive-pid-speed-control-for","title":"Optimized self-adaptive PID speed control for autonomous vehicles","date":"2021-09-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/estimation-of-road-boundary-for-intelligent","slug":"estimation-of-road-boundary-for-intelligent","title":"Estimation of Road Boundary for Intelligent Vehicles Based on DeepLabV3+ Architecture","date":"2021-08-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/signal-injection-attacks-against-ccd-image","slug":"signal-injection-attacks-against-ccd-image","title":"Signal Injection Attacks against CCD Image Sensors","date":"2021-08-19","arxiv_id":"2108.08881","repositories_listed":1,"syntology":null},{"url":"/paper/continuous-time-spatiotemporal-calibration-of","slug":"continuous-time-spatiotemporal-calibration-of","title":"Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera---IMU System","date":"2021-08-16","arxiv_id":"2108.07200","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-adversarial-attacks-on-driving","slug":"evaluating-adversarial-attacks-on-driving","title":"Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles","date":"2021-08-06","arxiv_id":"2108.02940","repositories_listed":1,"syntology":null},{"url":"/paper/toward-improving-confidence-in-autonomous","slug":"toward-improving-confidence-in-autonomous","title":"Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems","date":"2021-08-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/agent-aware-state-estimation-in-autonomous","slug":"agent-aware-state-estimation-in-autonomous","title":"Agent-aware State Estimation in Autonomous Vehicles","date":"2021-08-01","arxiv_id":"2108.00366","repositories_listed":1,"syntology":null},{"url":"/paper/human-trajectory-prediction-via","slug":"human-trajectory-prediction-via","title":"Human Trajectory Prediction via Counterfactual Analysis","date":"2021-07-29","arxiv_id":"2107.14202","repositories_listed":1,"syntology":null},{"url":"/paper/the-reasonable-crowd-towards-evidence-based","slug":"the-reasonable-crowd-towards-evidence-based","title":"The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior","date":"2021-07-28","arxiv_id":"2107.13507","repositories_listed":1,"syntology":null},{"url":"/paper/finding-failures-in-high-fidelity-simulation","slug":"finding-failures-in-high-fidelity-simulation","title":"Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm","date":"2021-07-27","arxiv_id":"2107.12940","repositories_listed":1,"syntology":null},{"url":"/paper/traffic4d-single-view-reconstruction-of","slug":"traffic4d-single-view-reconstruction-of","title":"Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision","date":"2021-07-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/arc-adversarially-robust-control-policies-for","slug":"arc-adversarially-robust-control-policies-for","title":"ARC: Adversarially Robust Control Policies for Autonomous Vehicles","date":"2021-07-09","arxiv_id":"2107.04487","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/arc-adversarially-robust-control-policies-for#ran","syntology_url":"https://syntology.ai/paper/2107.04487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.04487"}},"official":{"repos":["sampo-kuutti/adversarially-robust-control"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-interaction-aware-guidance-policies","slug":"learning-interaction-aware-guidance-policies","title":"Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios","date":"2021-07-09","arxiv_id":"2107.04538","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-model-for-inferring-a-spatial-road","slug":"learning-a-model-for-inferring-a-spatial-road","title":"Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision","date":"2021-07-05","arxiv_id":"2107.01784","repositories_listed":1,"syntology":null},{"url":"/paper/nuplan-a-closed-loop-ml-based-planning","slug":"nuplan-a-closed-loop-ml-based-planning","title":"NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles","date":"2021-06-22","arxiv_id":"2106.11810","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nuplan-a-closed-loop-ml-based-planning#ran","syntology_url":"https://syntology.ai/paper/2106.11810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11810"}},"official":null}},{"url":"/paper/a-dynamic-spatial-temporal-attention-network","slug":"a-dynamic-spatial-temporal-attention-network","title":"A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents","date":"2021-06-18","arxiv_id":"2106.10197","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-dynamic-spatial-temporal-attention-network#ran","syntology_url":"https://syntology.ai/paper/2106.10197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10197"}},"official":{"repos":["monjurulkarim/DSTA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/attdlnet-attention-based-dl-network-for-3d","slug":"attdlnet-attention-based-dl-network-for-3d","title":"AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition","date":"2021-06-17","arxiv_id":"2106.09637","repositories_listed":1,"syntology":null},{"url":"/paper/deterministic-iteratively-built-kd-tree-with","slug":"deterministic-iteratively-built-kd-tree-with","title":"Deterministic Iteratively Built KD-Tree with KNN Search for Exact Applications","date":"2021-06-07","arxiv_id":"2106.03799","repositories_listed":1,"syntology":null},{"url":"/paper/pylot-a-modular-platform-for-exploring-1","slug":"pylot-a-modular-platform-for-exploring-1","title":"Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles","date":"2021-05-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/reducing-dnn-properties-to-enable","slug":"reducing-dnn-properties-to-enable","title":"Reducing DNN Properties to Enable Falsification with Adversarial Attacks","date":"2021-05-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dslr-dynamic-to-static-lidar-scan","slug":"dslr-dynamic-to-static-lidar-scan","title":"DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder","date":"2021-05-26","arxiv_id":"2105.12774","repositories_listed":1,"syntology":null},{"url":"/paper/mth-ids-a-multi-tiered-hybrid-intrusion","slug":"mth-ids-a-multi-tiered-hybrid-intrusion","title":"MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles","date":"2021-05-26","arxiv_id":"2105.13289","repositories_listed":1,"syntology":null},{"url":"/paper/m4depth-a-motion-based-approach-for-monocular","slug":"m4depth-a-motion-based-approach-for-monocular","title":"M4Depth: Monocular depth estimation for autonomous vehicles in unseen environments","date":"2021-05-20","arxiv_id":"2105.09847","repositories_listed":1,"syntology":null},{"url":"/paper/coupling-intent-and-action-for-pedestrian","slug":"coupling-intent-and-action-for-pedestrian","title":"Coupling Intent and Action for Pedestrian Crossing Behavior Prediction","date":"2021-05-10","arxiv_id":"2105.04133","repositories_listed":1,"syntology":null},{"url":"/paper/novelty-detection-and-analysis-of-traffic","slug":"novelty-detection-and-analysis-of-traffic","title":"Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder","date":"2021-05-05","arxiv_id":"2105.01924","repositories_listed":1,"syntology":null},{"url":"/paper/improving-perception-via-sensor-placement","slug":"improving-perception-via-sensor-placement","title":"Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving","date":"2021-05-02","arxiv_id":"2105.00373","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/improving-perception-via-sensor-placement#ran","syntology_url":"https://syntology.ai/paper/2105.00373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.00373"}},"official":{"repos":["HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-example-detection-for-dnn-models","slug":"adversarial-example-detection-for-dnn-models","title":"Adversarial Example Detection for DNN Models: A Review and Experimental Comparison","date":"2021-05-01","arxiv_id":"2105.00203","repositories_listed":1,"syntology":null},{"url":"/paper/lane-graph-estimation-for-scene-understanding","slug":"lane-graph-estimation-for-scene-understanding","title":"Lane Graph Estimation for Scene Understanding in Urban Driving","date":"2021-05-01","arxiv_id":"2105.00195","repositories_listed":1,"syntology":null},{"url":"/paper/maneuver-aware-pooling-for-vehicle-trajectory","slug":"maneuver-aware-pooling-for-vehicle-trajectory","title":"Maneuver-Aware Pooling for Vehicle Trajectory Prediction","date":"2021-04-29","arxiv_id":"2104.14079","repositories_listed":1,"syntology":null},{"url":"/paper/robust-sensor-fusion-algorithms-against","slug":"robust-sensor-fusion-algorithms-against","title":"Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles","date":"2021-04-20","arxiv_id":"2104.09872","repositories_listed":1,"syntology":null},{"url":"/paper/minkloc-lidar-and-monocular-image-fusion-for","slug":"minkloc-lidar-and-monocular-image-fusion-for","title":"MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition","date":"2021-04-12","arxiv_id":"2104.05327","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/minkloc-lidar-and-monocular-image-fusion-for#ran","syntology_url":"https://syntology.ai/paper/2104.05327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.05327"}},"official":{"repos":["jac99/MinkLocMultimodal"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/raindrops-on-windshield-dataset-and","slug":"raindrops-on-windshield-dataset-and","title":"Raindrops on Windshield: Dataset and Lightweight Gradient-Based Detection Algorithm","date":"2021-04-11","arxiv_id":"2104.05078","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-object-detection-for-autonomous","slug":"enhancing-object-detection-for-autonomous","title":"Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance","date":"2021-04-08","arxiv_id":"2104.03888","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/enhancing-object-detection-for-autonomous#ran","syntology_url":"https://syntology.ai/paper/2104.03888","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.03888"}},"official":{"repos":["carranza96/waymo-detection-optimization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/icurb-imitation-learning-based-detection-of","slug":"icurb-imitation-learning-based-detection-of","title":"iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving","date":"2021-03-31","arxiv_id":"2103.17118","repositories_listed":1,"syntology":null},{"url":"/paper/topo-boundary-a-benchmark-dataset-on","slug":"topo-boundary-a-benchmark-dataset-on","title":"Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving","date":"2021-03-31","arxiv_id":"2103.17119","repositories_listed":1,"syntology":null},{"url":"/paper/fooling-lidar-perception-via-adversarial","slug":"fooling-lidar-perception-via-adversarial","title":"Fooling LiDAR Perception via Adversarial Trajectory Perturbation","date":"2021-03-29","arxiv_id":"2103.15326","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fooling-lidar-perception-via-adversarial#ran","syntology_url":"https://syntology.ai/paper/2103.15326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15326"}},"official":null}},{"url":"/paper/sienet-spatial-information-enhancement","slug":"sienet-spatial-information-enhancement","title":"SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud","date":"2021-03-29","arxiv_id":"2103.15396","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-self-supervised-monocular-depth","slug":"revisiting-self-supervised-monocular-depth","title":"Revisiting Self-Supervised Monocular Depth Estimation","date":"2021-03-23","arxiv_id":"2103.12496","repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-road-vanishing-point-detection","slug":"a-robust-road-vanishing-point-detection","title":"A Robust Road Vanishing Point Detection Adapted to the Real-World Driving Scenes","date":"2021-03-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-reinforcement-learning-for-1","slug":"weakly-supervised-reinforcement-learning-for-1","title":"Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages","date":"2021-03-17","arxiv_id":"2103.09726","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-the-robustness-of-skeleton","slug":"understanding-the-robustness-of-skeleton","title":"Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack","date":"2021-03-09","arxiv_id":"2103.05347","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/understanding-the-robustness-of-skeleton#ran","syntology_url":"https://syntology.ai/paper/2103.05347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05347"}},"official":{"repos":["realcrane/SMART"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sequential-place-learning-heuristic-free-high","slug":"sequential-place-learning-heuristic-free-high","title":"Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition","date":"2021-03-02","arxiv_id":"2103.02074","repositories_listed":1,"syntology":null},{"url":"/paper/dr-tanet-dynamic-receptive-temporal-attention","slug":"dr-tanet-dynamic-receptive-temporal-attention","title":"DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection","date":"2021-03-01","arxiv_id":"2103.00879","repositories_listed":1,"syntology":null},{"url":"/paper/finding-the-gap-neuromorphic-motion-vision-in","slug":"finding-the-gap-neuromorphic-motion-vision-in","title":"Finding the Gap: Neuromorphic Motion Vision in Cluttered Environments","date":"2021-02-16","arxiv_id":"2102.08417","repositories_listed":1,"syntology":null},{"url":"/paper/robust-lane-detection-via-expanded-self","slug":"robust-lane-detection-via-expanded-self","title":"Robust Lane Detection via Expanded Self Attention","date":"2021-02-14","arxiv_id":"2102.07037","repositories_listed":1,"syntology":null},{"url":"/paper/driving-style-representation-in-convolutional","slug":"driving-style-representation-in-convolutional","title":"Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification","date":"2021-02-11","arxiv_id":"2102.05843","repositories_listed":1,"syntology":null},{"url":"/paper/zeroscatter-domain-transfer-for-long-distance","slug":"zeroscatter-domain-transfer-for-long-distance","title":"ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media","date":"2021-02-11","arxiv_id":"2102.05847","repositories_listed":1,"syntology":null},{"url":"/paper/object-tracking-by-detection-with-visual-and","slug":"object-tracking-by-detection-with-visual-and","title":"Object Tracking by Detection with Visual and Motion Cues","date":"2021-01-19","arxiv_id":"2101.07549","repositories_listed":1,"syntology":null},{"url":"/paper/distributionally-consistent-simulation-of","slug":"distributionally-consistent-simulation-of","title":"Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing","date":"2021-01-08","arxiv_id":"2101.02828","repositories_listed":1,"syntology":null},{"url":"/paper/fdmt-a-benchmark-dataset-for-fine-grained","slug":"fdmt-a-benchmark-dataset-for-fine-grained","title":"FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation","date":"2020-12-31","arxiv_id":"2012.15717","repositories_listed":1,"syntology":null},{"url":"/paper/low-latency-perception-in-off-road-dynamical","slug":"low-latency-perception-in-off-road-dynamical","title":"Low-latency Perception in Off-Road Dynamical Low Visibility Environments","date":"2020-12-23","arxiv_id":"2012.13014","repositories_listed":1,"syntology":null},{"url":"/paper/video-deblurring-by-fitting-to-test-data","slug":"video-deblurring-by-fitting-to-test-data","title":"Video Deblurring by Fitting to Test Data","date":"2020-12-09","arxiv_id":"2012.05228","repositories_listed":1,"syntology":null},{"url":"/paper/leading-cruise-control-in-mixed-traffic-flow-1","slug":"leading-cruise-control-in-mixed-traffic-flow-1","title":"Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability","date":"2020-12-08","arxiv_id":"2012.04313","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-bird-s-eye-view-semantic-hd","slug":"understanding-bird-s-eye-view-semantic-hd","title":"Understanding Bird's-Eye View of Road Semantics using an Onboard Camera","date":"2020-12-05","arxiv_id":"2012.03040","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-32-pedestrian-attributes-for","slug":"detecting-32-pedestrian-attributes-for","title":"Detecting 32 Pedestrian Attributes for Autonomous Vehicles","date":"2020-12-04","arxiv_id":"2012.02647","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-pyramid-network-for-pedestrian","slug":"temporal-pyramid-network-for-pedestrian","title":"Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision","date":"2020-12-03","arxiv_id":"2012.01884","repositories_listed":1,"syntology":null},{"url":"/paper/why-model-why-assessing-the-strengths-and","slug":"why-model-why-assessing-the-strengths-and","title":"Why model why? Assessing the strengths and limitations of LIME","date":"2020-11-30","arxiv_id":"2012.00093","repositories_listed":1,"syntology":null},{"url":"/paper/polarization-driven-semantic-segmentation-via","slug":"polarization-driven-semantic-segmentation-via","title":"Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion","date":"2020-11-26","arxiv_id":"2011.13313","repositories_listed":1,"syntology":null},{"url":"/paper/emergent-road-rules-in-multi-agent-driving-1","slug":"emergent-road-rules-in-multi-agent-driving-1","title":"Emergent Road Rules In Multi-Agent Driving Environments","date":"2020-11-21","arxiv_id":"2011.10753","repositories_listed":1,"syntology":null},{"url":"/paper/robust-super-resolution-depth-imaging-via-a","slug":"robust-super-resolution-depth-imaging-via-a","title":"Robust super-resolution depth imaging via a multi-feature fusion deep network","date":"2020-11-20","arxiv_id":"2011.11444","repositories_listed":1,"syntology":null},{"url":"/paper/deepseqslam-a-trainable-cnn-rnn-for-joint","slug":"deepseqslam-a-trainable-cnn-rnn-for-joint","title":"DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition","date":"2020-11-17","arxiv_id":"2011.08518","repositories_listed":1,"syntology":null},{"url":"/paper/gndnet-fast-ground-plane-estimation-and-point","slug":"gndnet-fast-ground-plane-estimation-and-point","title":"GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles","date":"2020-11-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-planning-for-autonomous-vehicles","slug":"trajectory-planning-for-autonomous-vehicles","title":"Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning","date":"2020-11-09","arxiv_id":"2011.04752","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-mixed-autonomy-traffic-flow-with","slug":"optimizing-mixed-autonomy-traffic-flow-with","title":"Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL","date":"2020-10-30","arxiv_id":"2011.00120","repositories_listed":1,"syntology":null},{"url":"/paper/pedestrian-intention-prediction-a-multi-task","slug":"pedestrian-intention-prediction-a-multi-task","title":"Pedestrian Intention Prediction: A Multi-task Perspective","date":"2020-10-20","arxiv_id":"2010.10270","repositories_listed":1,"syntology":null},{"url":"/paper/roneld-robust-neural-network-output","slug":"roneld-robust-neural-network-output","title":"RONELD: Robust Neural Network Output Enhancement for Active Lane Detection","date":"2020-10-19","arxiv_id":"2010.09548","repositories_listed":1,"syntology":null},{"url":"/paper/neural-circuit-policies-enabling-auditable","slug":"neural-circuit-policies-enabling-auditable","title":"Neural circuit policies enabling auditable autonomy","date":"2020-10-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-automotive-radar-data-acquisition-1","slug":"adaptive-automotive-radar-data-acquisition-1","title":"Automotive Radar Data Acquisition using Object Detection","date":"2020-10-05","arxiv_id":"2010.02367","repositories_listed":1,"syntology":null},{"url":"/paper/solution-concepts-in-hierarchical-games-with","slug":"solution-concepts-in-hierarchical-games-with","title":"Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving","date":"2020-09-21","arxiv_id":"2009.10033","repositories_listed":1,"syntology":null},{"url":"/paper/performance-monitoring-of-object-detection","slug":"performance-monitoring-of-object-detection","title":"Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment","date":"2020-09-18","arxiv_id":"2009.08650","repositories_listed":1,"syntology":null},{"url":"/paper/horus-using-sensor-fusion-to-combine","slug":"horus-using-sensor-fusion-to-combine","title":"Horus: Using Sensor Fusion to Combine Infrastructure and On-board Sensing to Improve Autonomous Vehicle Safety","date":"2020-09-07","arxiv_id":"2009.03458","repositories_listed":1,"syntology":null},{"url":"/paper/radar-rgb-attentive-fusion-for-robust-object","slug":"radar-rgb-attentive-fusion-for-robust-object","title":"Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous Vehicles","date":"2020-08-31","arxiv_id":"2008.13642","repositories_listed":1,"syntology":null},{"url":"/paper/deepsocial-social-distancing-monitoring-and","slug":"deepsocial-social-distancing-monitoring-and","title":"DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic","date":"2020-08-26","arxiv_id":"2008.11672","repositories_listed":1,"syntology":null},{"url":"/paper/what-if-motion-prediction-for-autonomous","slug":"what-if-motion-prediction-for-autonomous","title":"What-If Motion Prediction for Autonomous Driving","date":"2020-08-24","arxiv_id":"2008.10587","repositories_listed":1,"syntology":null},{"url":"/paper/issafe-improving-semantic-segmentation-in","slug":"issafe-improving-semantic-segmentation-in","title":"ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data","date":"2020-08-20","arxiv_id":"2008.08974","repositories_listed":1,"syntology":null},{"url":"/paper/lift-splat-shoot-encoding-images-from","slug":"lift-splat-shoot-encoding-images-from","title":"Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D","date":"2020-08-13","arxiv_id":"2008.05711","repositories_listed":1,"syntology":null},{"url":"/paper/traffic-control-gesture-recognition-for","slug":"traffic-control-gesture-recognition-for","title":"Traffic Control Gesture Recognition for Autonomous Vehicles","date":"2020-07-31","arxiv_id":"2007.16072","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-lidar-sampling-and-depth-completion","slug":"adaptive-lidar-sampling-and-depth-completion","title":"Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance","date":"2020-07-27","arxiv_id":"2007.13834","repositories_listed":1,"syntology":null},{"url":"/paper/gsnet-joint-vehicle-pose-and-shape","slug":"gsnet-joint-vehicle-pose-and-shape","title":"GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision","date":"2020-07-26","arxiv_id":"2007.13124","repositories_listed":1,"syntology":null},{"url":"/paper/kprnet-improving-projection-based-lidar","slug":"kprnet-improving-projection-based-lidar","title":"KPRNet: Improving projection-based LiDAR semantic segmentation","date":"2020-07-24","arxiv_id":"2007.12668","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/kprnet-improving-projection-based-lidar#ran","syntology_url":"https://syntology.ai/paper/2007.12668","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12668"}},"official":{"repos":["DeyvidKochanov-TomTom/kprnet"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-object-detection-through-an","slug":"understanding-object-detection-through-an","title":"Understanding Object Detection Through An Adversarial Lens","date":"2020-07-11","arxiv_id":"2007.05828","repositories_listed":1,"syntology":null},{"url":"/paper/multi-agent-routing-value-iteration-network","slug":"multi-agent-routing-value-iteration-network","title":"Multi-Agent Routing Value Iteration Network","date":"2020-07-09","arxiv_id":"2007.05096","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/multi-agent-routing-value-iteration-network#ran","syntology_url":"https://syntology.ai/paper/2007.05096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05096"}},"official":{"repos":["uber/MARVIN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pathgan-local-path-planning-with-generative","slug":"pathgan-local-path-planning-with-generative","title":"PathGAN: Local Path Planning with Attentive Generative Adversarial Networks","date":"2020-07-08","arxiv_id":"2007.03877","repositories_listed":1,"syntology":null},{"url":"/paper/depthnet-real-time-lidar-point-cloud-depth","slug":"depthnet-real-time-lidar-point-cloud-depth","title":"DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles","date":"2020-07-05","arxiv_id":"2007.02438","repositories_listed":1,"syntology":null},{"url":"/paper/regulating-accuracy-efficiency-trade-offs-in","slug":"regulating-accuracy-efficiency-trade-offs-in","title":"Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems","date":"2020-07-04","arxiv_id":"2007.02203","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-traffic-signal-control-using-queue","slug":"a-simple-traffic-signal-control-using-queue","title":"A Simple Traffic Signal Control Using Queue Length Information","date":"2020-06-11","arxiv_id":"2006.06337","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-under-moral","slug":"reinforcement-learning-under-moral","title":"Reinforcement Learning Under Moral Uncertainty","date":"2020-06-08","arxiv_id":"2006.04734","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reinforcement-learning-under-moral#ran","syntology_url":"https://syntology.ai/paper/2006.04734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04734"}},"official":{"repos":["uber-research/normative-uncertainty"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-reinforcement-learning-for-human-like","slug":"deep-reinforcement-learning-for-human-like","title":"Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars","date":"2020-06-07","arxiv_id":"2006.04218","repositories_listed":1,"syntology":null},{"url":"/paper/mantra-memory-augmented-networks-for-multiple-1","slug":"mantra-memory-augmented-networks-for-multiple-1","title":"MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction","date":"2020-06-05","arxiv_id":"2006.03340","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning","slug":"reinforcement-learning","title":"Reinforcement Learning","date":"2020-05-29","arxiv_id":"2005.14419","repositories_listed":1,"syntology":null},{"url":"/paper/fast-risk-assessment-for-autonomous-vehicles","slug":"fast-risk-assessment-for-autonomous-vehicles","title":"Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures","date":"2020-05-27","arxiv_id":"2005.13458","repositories_listed":1,"syntology":null},{"url":"/paper/carpe-posterum-a-convolutional-approach-for","slug":"carpe-posterum-a-convolutional-approach-for","title":"CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction","date":"2020-05-26","arxiv_id":"2005.12469","repositories_listed":1,"syntology":null},{"url":"/paper/vpr-bench-an-open-source-visual-place","slug":"vpr-bench-an-open-source-visual-place","title":"VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change","date":"2020-05-17","arxiv_id":"2005.08135","repositories_listed":1,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/vpr-bench-an-open-source-visual-place#ran","syntology_url":"https://syntology.ai/paper/2005.08135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08135"}},"official":{"repos":["MubarizZaffar/VPR-Bench"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/persistent-map-saving-for-visual-localization","slug":"persistent-map-saving-for-visual-localization","title":"Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension","date":"2020-05-15","arxiv_id":"2005.07429","repositories_listed":1,"syntology":null}],"record_sha256":"3dc794bf09b7a5b75a8015370654fd916b83a91d04535eedff13248950f19a76","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}