ParkingScenes is a new multimodal dataset of 704 structured reverse and parallel parking episodes generated in CARLA with Hybrid A* and MPC trajectories, showing better model performance than unstructured simulation data.
A Commute in Data: The comma2k19 Dataset
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
comma.ai presents comma2k19, a dataset of over 33 hours of commute in California's 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS measurements and all CAN data sent by the car with a comma grey panda. Laika, an open-source GNSS processing library, is also introduced here. Laika produces 40% more accurate positions than the GNSS module used to collect the raw data. This dataset includes pose (position + orientation) estimates in a global reference frame of the recording camera. These poses were computed with a tightly coupled INS/GNSS/Vision optimizer that relies on data processed by Laika. comma2k19 is ideal for development and validation of tightly coupled GNSS algorithms and mapping algorithms that work with commodity sensors.
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A survey synthesizing sensor fusion strategies, AV datasets, and emerging LLM/VLM-powered object detection pipelines for autonomous vehicles.
citing papers explorer
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ParkingScenes: A Structured Dataset for End-to-End Autonomous Parking in Simulation Scenes
ParkingScenes is a new multimodal dataset of 704 structured reverse and parallel parking episodes generated in CARLA with Hybrid A* and MPC trajectories, showing better model performance than unstructured simulation data.
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All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles
A survey synthesizing sensor fusion strategies, AV datasets, and emerging LLM/VLM-powered object detection pipelines for autonomous vehicles.