{"id":58,"date":"2026-05-05T19:58:21","date_gmt":"2026-05-05T19:58:21","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/?page_id=58"},"modified":"2026-05-07T15:40:30","modified_gmt":"2026-05-07T15:40:30","slug":"method","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/method\/","title":{"rendered":"Method"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>Simulation Based Data Augmentation <\/strong><\/h2>\n\n\n\n<p>We generate long-tail scenarios by modifying original nuPlan logs using InterPlan, simulate them in nuPlan Devkit simulator, convert valid rollouts to rasterized representation and <strong>retrain RAP<\/strong> on the resulting dataset.&nbsp;<\/p>\n\n\n\n<p>We focus on generating the following long-tail scenarios: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pedestrian focused scenario modification (primary)&nbsp;<\/li>\n\n\n\n<li>Construction zone blockage<\/li>\n\n\n\n<li>Overtaking stopped\/parked vehicle<\/li>\n\n\n\n<li>Accident site navigation&nbsp;<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"573\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-2-1024x573.png\" alt=\"\" class=\"wp-image-60\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-2-1024x573.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-2-300x168.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-2-768x430.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-2.png 1142w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\">Closed loop simulation setup showing the generation of modified long tail scenario data used to retrain RAP <\/figcaption><\/figure>\n<\/div>\n\n\n<p>Rollouts from simulations are filtered to ensure only high quality ground truth trajectories are included in retraining data using the following criteria :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Collision detection<\/li>\n\n\n\n<li>Invalid state filtering<\/li>\n\n\n\n<li>Smoothness metrics (jerk)<\/li>\n\n\n\n<li>Traffic lane compliance<\/li>\n<\/ul>\n\n\n\n<p>We plan to use an ensemble of rule based and learned policies to predict and filter the best rollouts from each scenario. The initial results below are using rule based policy PDM-closed<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"658\" height=\"379\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-16.png\" alt=\"\" class=\"wp-image-141\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-16.png 658w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-16-300x173.png 300w\" sizes=\"auto, (max-width: 658px) 100vw, 658px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Analysis Overview<\/strong><\/h2>\n\n\n\n<p>We investigate RAP&#8217;s failure modes through off-policy analysis, probing its behaviour on edited scenes across three categories: pedestrians, traffic signs, and unguarded obstacles.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Pedestrian Analysis<\/strong><\/h2>\n\n\n\n<p>Synthetic jaywalking pedestrians were inserted into front camera RGB of 48 nuPlan scenes using the Gemini API, producing 144 edited scenes across three density conditions. Using LiDAR depth, we computed the required deceleration to stop safely for each scene and bucketed responses into comfortable, moderate, hard, and emergency braking categories.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"296\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-3-1024x296.png\" alt=\"\" class=\"wp-image-61\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-3-1024x296.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-3-300x87.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-3-768x222.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-3.png 1201w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\"><strong>Pedestrians inserted into nuPlan front camera RGB using Gemini.<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"416\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-4-1024x416.png\" alt=\"\" class=\"wp-image-62\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-4-1024x416.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-4-300x122.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-4-768x312.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-4.png 1265w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\"><strong>Required deceleration formula and braking effort categories used for feasibility analysis.<\/strong><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Obstacle Analysis<\/strong><\/h2>\n\n\n\n<p>We generated synthetic visual interventions by inserting physical obstacles into nuPlan RGB scenes across categories including broken down cars, road blockages, debris on road, and lane closures. RAP inference was run on both the original and edited RGB, and trajectory deviation was measured using ADE\/v to assess whether the model reacts to each obstacle type.<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"70\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_small_debris_on_lane-1024x576.jpg\" alt=\"\" class=\"wp-image-70\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_small_debris_on_lane-1024x576.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_small_debris_on_lane-300x169.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_small_debris_on_lane-768x432.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_small_debris_on_lane-1536x864.jpg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_small_debris_on_lane.jpg 1920w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\">small debris<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"81\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_debris_on_lane-1024x576.jpg\" alt=\"\" class=\"wp-image-81\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_debris_on_lane-1024x576.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_debris_on_lane-300x169.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_debris_on_lane-768x432.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_debris_on_lane-1536x864.jpg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_debris_on_lane.jpg 1920w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\">large debris<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"78\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_front_pothole_road_damage-1024x576.jpg\" alt=\"\" class=\"wp-image-78\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_front_pothole_road_damage-1024x576.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_front_pothole_road_damage-300x169.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_front_pothole_road_damage-768x432.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_front_pothole_road_damage-1536x864.jpg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/32e05d8aad845364_front_pothole_road_damage.jpg 1920w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\">pothole<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"79\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_lane_blockage-1024x576.jpg\" alt=\"\" class=\"wp-image-79\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_lane_blockage-1024x576.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_lane_blockage-300x169.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_lane_blockage-768x432.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_lane_blockage-1536x864.jpg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/69ce2df0a41a5a83_front_lane_blockage.jpg 1920w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\">road blocked<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"80\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/74f5008bede15217_front_broken_down_car-1024x576.jpg\" alt=\"\" class=\"wp-image-80\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/74f5008bede15217_front_broken_down_car-1024x576.jpg 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/74f5008bede15217_front_broken_down_car-300x169.jpg 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/74f5008bede15217_front_broken_down_car-768x432.jpg 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/74f5008bede15217_front_broken_down_car-1536x864.jpg 1536w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/74f5008bede15217_front_broken_down_car.jpg 1920w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\">broken-down car<\/figcaption><\/figure>\n<\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Traffic Sign Analysis<\/strong><\/h2>\n\n\n\n<p>Following the same visual intervention approach, we inserted traffic signs including stop signs, speed limit signs, and school zone markers into nuPlan RGB scenes and measured RAP&#8217;s trajectory deviation using ADE\/v. <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"976\" height=\"538\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-12.png\" alt=\"\" class=\"wp-image-86\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-12.png 976w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-12-300x165.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-12-768x423.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\"><strong>Two-stage pipeline: DINO detection followed by EfficientNet classification, then projected onto raster view.<\/strong><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"511\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-11-1024x511.png\" alt=\"\" class=\"wp-image-85\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-11-1024x511.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-11-300x150.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-11-768x383.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/wp-content\/uploads\/sites\/152\/2026\/05\/image-11.png 1156w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><figcaption class=\"wp-element-caption\"><strong>Geometric representations used to encode each sign type onto the raster view.<\/strong><\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Simulation Based Data Augmentation We generate long-tail scenarios by modifying original nuPlan logs using InterPlan, simulate them in nuPlan Devkit simulator, convert valid rollouts to rasterized representation and retrain RAP on the resulting dataset.&nbsp; We focus on generating the following long-tail scenarios: Rollouts from simulations are filtered to ensure only high quality ground truth trajectories &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/method\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Method&#8221;<\/span><\/a><\/p>\n","protected":false},"author":286,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-58","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Method - Rasterized Representations for Autonomous Driving<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf10\/method\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Method - Rasterized Representations for Autonomous Driving\" \/>\n<meta property=\"og:description\" content=\"Simulation Based Data Augmentation We generate 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