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Found 23 papers, 6 papers with code
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on
3D Object Detection
on OPV2V
Learning to Reweight Examples for Robust Deep Learning
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns.
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms.
Differentiable Compositional Kernel Learning for Gaussian Processes
The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel.
Incorporating Relation Paths in Neural Relation Extraction
Distantly supervised relation extraction has been widely used to find novel relational facts from plain text.
End-to-end Interpretable Neural Motion Planner
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users.
Efficient Summarization with Read-Again and Copy Mechanism
Towards this goal, we first introduce a simple mechanism that first reads the input sequence before committing to a representation of each word.
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles.
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
DSDNet: Deep Structured self-Driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network.
End-to-end Contextual Perception and Prediction with Interaction Transformer
We show that our approach can outperform the state-of-the-art on both datasets.
Weakly-supervised 3D Shape Completion in the Wild
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned.
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
In this paper, we propose an end-to-end self-driving network featuring a sparse attention module that learns to automatically attend to important regions of the input.
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.
Deep Structured Reactive Planning
An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene.
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
In this paper, we propose LaneRCNN, a graph-centric motion forecasting model.
Ranked #150 on
Motion Forecasting
on Argoverse CVPR 2020
Network Automatic Pruning: Start NAP and Take a Nap
This process is typically time-consuming and requires expert knowledge to achieve good results.
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
The key idea is to decompose the 4D object label into two parts: the object size in 3D that's fixed through time for rigid objects, and the motion path describing the evolution of the object's pose through time.
Self-Supervised Representation Learning from Flow Equivariance
Motivated by this ability, we present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes with many moving objects.
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression
Model compression can significantly reduce the computation and memory footprint of large neural networks.
Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching
Current weakly supervised semantic segmentation (WSSS) frameworks usually contain the separated mask-refinement model and the main semantic region mining model.
Knowledge Distillation
Weakly supervised Semantic Segmentation
+1
Rethinking Closed-loop Training for Autonomous Driving
Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v. s.
