research
∙
08/24/2023
On Offline Evaluation of 3D Object Detection for Autonomous Driving
Prior work in 3D object detection evaluates models using offline metrics...
share
research
∙
06/29/2023
End-to-end Autonomous Driving: Challenges and Frontiers
The autonomous driving community has witnessed a rapid growth in approac...
research
∙
06/13/2023
Parting with Misconceptions about Learning-based Vehicle Motion Planning
The release of nuPlan marks a new era in vehicle motion planning researc...
research
∙
06/13/2023
Hidden Biases of End-to-End Driving Models
End-to-end driving systems have recently made rapid progress, in particu...
research
∙
10/25/2022
PlanT: Explainable Planning Transformers via Object-Level Representations
Planning an optimal route in a complex environment requires efficient re...
research
∙
05/31/2022
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
How should we integrate representations from complementary sensors for a...
research
∙
04/28/2022
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Simulators offer the possibility of safe, low-cost development of self-d...
research
∙
11/01/2021
Projected GANs Converge Faster
Generative Adversarial Networks (GANs) produce high-quality images but a...
research
∙
09/09/2021
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Efficient reasoning about the semantic, spatial, and temporal structure ...
research
∙
04/19/2021
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
How should representations from complementary sensors be integrated for ...
research
∙
06/12/2020
Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects is a crucial prerequisite for r...
research
∙
05/20/2020
Label Efficient Visual Abstractions for Autonomous Driving
It is well known that semantic segmentation can be used as an effective ...
research
∙
04/09/2020
Scalable Active Learning for Object Detection
Deep Neural Networks trained in a fully supervised fashion are the domin...
research
∙
07/27/2019
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
Semantic segmentation with Convolutional Neural Networks is a memory-int...
research
∙
05/29/2019
Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling
Deep Neural Networks (DNNs) often rely on very large datasets for traini...
research
∙
11/08/2018
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Annotating the right data for training deep neural networks is an import...
research
∙
11/08/2018
Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels
Training deep networks for semantic segmentation requires annotation of ...
research
∙
11/06/2018
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scala...
research
∙
06/01/2018
Targeted Kernel Networks: Faster Convolutions with Attentive Regularization
We propose Attentive Regularization (AR), a method to constrain the acti...
research
∙
05/19/2018
