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Found 5 papers, 2 papers with code
S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations
Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D.
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Simulators offer the possibility of safe, low-cost development of self-driving systems.
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation
In order to handle the challenges of autonomous driving, deep learning has proven to be crucial in tackling increasingly complex tasks, such as 3D detection or instance segmentation.
Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes
Object detection is an important task in environment perception for autonomous driving.
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