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Found 18 papers, 8 papers with code
Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.
MonoEdge: Monocular 3D Object Detection Using Local Perspectives
We propose a novel approach for monocular 3D object detection by leveraging local perspective effects of each object.
E2PN: Efficient SE(3)-Equivariant Point Network
We also propose a permutation layer to recover SO(3) features from spherical features to preserve the capacity to distinguish rotations.
CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving
The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving.
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
The performance of PEARL$^+$ is validated by solving three safety-critical problems related to robots and AVs, including two MuJoCo benchmark problems.
Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations
We learn an embedding for each point cloud in a feature space that preserves the SO(3)-equivariance property, enabled by recent developments in equivariant neural networks.
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars
A unified neural network structure is presented for joint 3D object detection and point cloud segmentation in this paper.
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions.
Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera.
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function.
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
Object detection and tracking is a key task in autonomy.
SUPER: A Novel Lane Detection System
AI-based lane detection algorithms were actively studied over the last few years.
Monocular Depth Prediction through Continuous 3D Loss
In contrast to the current point-to-point loss evaluation approach, the proposed 3D loss treats point clouds as continuous objects; therefore, it compensates for the lack of dense ground truth depth due to LIDAR's sparsity measurements.
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
Mcity Data Collection for Automated Vehicles Study
The main goal of this paper is to introduce the data collection effort at Mcity targeting automated vehicle development.
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
We then use this model to reproduce the high-dimensional driving scenarios in a finitely tractable form.
Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research
We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA.
Physics and Society
Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments
A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions.
Systems and Control
