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Found 33 papers, 17 papers with code
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow
Yet the overall impact on traffic flow for this new class of planners remain to be understood.
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers.
Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels
We present a unified probabilistic model that learns a representative set of discrete vehicle actions and predicts the probability of each action given a particular scenario.
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents.
Learning Interactive Driving Policies via Data-driven Simulation
Data-driven simulators promise high data-efficiency for driving policy learning.
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
Searching for Efficient Multi-Stage Vision Transformers
This naturally raises the question of how the performance of ViT can be advanced with design techniques of CNN.
Efficient and Robust LiDAR-Based End-to-End Navigation
On the other hand, increasing the robustness of these systems is also critical; however, even estimating the model's uncertainty is very challenging due to the cost of sampling-based methods.
Feedback from Pixels: Output Regulation via Learning-Based Scene View Synthesis
We propose a novel controller synthesis involving feedback from pixels, whereby the measurement is a high dimensional signal representing a pixelated image with Red-Green-Blue (RGB) values.
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
Learning complex robot behaviors through interaction requires structured exploration.
Learning to Plan via Deep Optimistic Value Exploration
Deep exploration requires coordinated long-term planning.
Perception-aware time optimal path parameterization for quadrotors
The main contribution of this paper is an efficient time optimal path parametrization algorithm for quadrotors with limited field of view constraints.
Deep Orientation Uncertainty Learning based on a Bingham Loss
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
Our main contribution is the concept of learning context maps to improve the prediction task.
A Theory of Uncertainty Variables for State Estimation and Inference
Probability theory starts with a distribution function (equivalently a probability measure) as a primitive and builds all other useful concepts, such as law of total probability, Bayes' law, independence, graphical models, point estimate, on it.
Multi-resolution Low-rank Tensor Formats
We describe a simple, black-box compression format for tensors with a multiscale structure.
Numerical Analysis Numerical Analysis 65F99, 15A69
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex extrinsic dynamics are generated organically through the natural interactions of the vehicle.
Robotics
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
In this paper, we address the problem of fast depth estimation on embedded systems.
Invertibility of Convolutional Generative Networks from Partial Measurements
In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i. e., the recovery of input latent code given the network output).
Variational End-to-End Navigation and Localization
We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map.
The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale, aggressive indoor flight dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception. Inspired by the potential of future high-speed fully-autonomous drone racing, the Blackbird dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to $7. 0ms^-1$.
Tensor ring decomposition
Tensor decompositions such as the canonical format and the tensor train format have been widely utilized to reduce storage costs and operational complexities for high-dimensional data, achieving linear scaling with the input dimension instead of exponential scaling.
Numerical Analysis Numerical Analysis 65F99, 15A23, 15A69
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving.
Ranked #6 on
Depth Completion
on VOID
Spatial Uncertainty Sampling for End-to-End Control
Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.
A General Pipeline for 3D Detection of Vehicles
Autonomous driving requires 3D perception of vehicles and other objects in the in environment.
Ranked #17 on
3D Object Detection
on KITTI Cars Easy
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.
CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization
A well-established localization approach combines ray casting with a particle filter, leading to a computationally expensive algorithm that is difficult to run on resource-constrained mobile robots.
Data Structures and Algorithms Robotics
Sparse Depth Sensing for Resource-Constrained Robots
We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?
Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals
In this paper, we propose a coordination control algorithm for this problem, assuming stochastic models for the arrival times of the vehicles.
Sampling-based Algorithms for Optimal Motion Planning
The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i. e., such that the cost of the returned solution converges almost surely to the optimum.
Robotics
Incremental Sampling-based Algorithms for Optimal Motion Planning
Second, a new algorithm is considered, called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost of the best path in the RRG converges to the optimum almost surely.
Robotics 68T40
