Search Results for author:
Found 24 papers, 14 papers with code
SLEDGE: Synthesizing Simulation Environments for Driving Agents with Generative Models
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs.
Generalized Predictive Model for Autonomous Driving
In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline.
DriveLM: Driving with Graph Visual Question Answering
The experiments demonstrate that Graph VQA provides a simple, principled framework for reasoning about a driving scene, and DriveLM-Data provides a challenging benchmark for this task.
On Offline Evaluation of 3D Object Detection for Autonomous Driving
Prior work in 3D object detection evaluates models using offline metrics like average precision since closed-loop online evaluation on the downstream driving task is costly.
End-to-end Autonomous Driving: Challenges and Frontiers
The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction.
Parting with Misconceptions about Learning-based Vehicle Motion Planning
The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting.
Hidden Biases of End-to-End Driving Models
End-to-end driving systems have recently made rapid progress, in particular on CARLA.
Ranked #1 on
CARLA MAP Leaderboard
on CARLA
PlanT: Explainable Planning Transformers via Object-Level Representations
Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene.
Ranked #6 on
CARLA longest6
on CARLA
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Ranked #6 on
Autonomous Driving
on CARLA Leaderboard
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.
Projected GANs Converge Faster
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train.
Ranked #1 on
Image Generation
on Stanford Cars
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving.
Ranked #4 on
Novel View Synthesis
on X3D
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
How should representations from complementary sensors be integrated for autonomous driving?
Ranked #1 on
Autonomous Driving
on Town05 Short
Benchmarking Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding.
Label Efficient Visual Abstractions for Autonomous Driving
Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.
Scalable Active Learning for Object Detection
We have built a scalable production system for active learning in the domain of autonomous driving.
Training Data Distribution Search with Ensemble Active Learning
In this paper, we propose to scale up ensemble Active Learning methods to perform acquisition at a large scale (10k to 500k samples at a time).
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions.
Training Data Subset Search with Ensemble Active Learning
In this paper, we propose to scale up ensemble Active Learning (AL) methods to perform acquisition at a large scale (10k to 500k samples at a time).
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Annotating the right data for training deep neural networks is an important challenge.
Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels
With our design, the network progressively learns features specific to the target domain using annotation from only the source domain.
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN).
Targeted Kernel Networks: Faster Convolutions with Attentive Regularization
We propose Attentive Regularization (AR), a method to constrain the activation maps of kernels in Convolutional Neural Networks (CNNs) to specific regions of interest (ROIs).
Learning Sampling Policies for Domain Adaptation
We address the problem of semi-supervised domain adaptation of classification algorithms through deep Q-learning.
