Search Results for author: Sertac Karaman

Found 33 papers, 17 papers with code

Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models

no code implementations26 Oct 2023 Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus

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.

Autonomous Driving Data Augmentation

Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow

no code implementations23 Apr 2023 Noam Buckman, Sertac Karaman, Daniela Rus

Yet the overall impact on traffic flow for this new class of planners remain to be understood.

Autonomous Driving

Infrastructure-based End-to-End Learning and Prevention of Driver Failure

no code implementations21 Mar 2023 Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus

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.

Autonomous Vehicles

Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels

no code implementations14 Dec 2022 Charles Richter, Patrick R. Barragán, Sertac Karaman

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.

Clustering Variational Inference

Searching for Efficient Multi-Stage Vision Transformers

1 code implementation1 Sep 2021 Yi-Lun Liao, Sertac Karaman, Vivienne Sze

This naturally raises the question of how the performance of ViT can be advanced with design techniques of CNN.

Neural Architecture Search

Efficient and Robust LiDAR-Based End-to-End Navigation

no code implementations20 May 2021 Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela Rus

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

1 code implementation19 Mar 2021 Murad Abu-Khalaf, Sertac Karaman, Daniela Rus

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.

object-detection Object Detection

Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space

1 code implementation19 Feb 2021 Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus

We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.

Reinforcement Learning (RL)

Perception-aware time optimal path parameterization for quadrotors

no code implementations28 May 2020 Igor Spasojevic, Varun Murali, Sertac Karaman

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

1 code implementation ICLR 2020 Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus

Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.

Motion Estimation Pose Estimation

A Theory of Uncertainty Variables for State Estimation and Inference

no code implementations24 Sep 2019 Rajat Talak, Sertac Karaman, Eytan Modiano

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

1 code implementation29 Aug 2019 Oscar Mickelin, Sertac Karaman

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

6 code implementations27 May 2019 Winter Guerra, Ezra Tal, Varun Murali, Gilhyun Ryou, Sertac Karaman

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.


Invertibility of Convolutional Generative Networks from Partial Measurements

1 code implementation NeurIPS 2018 Fangchang Ma, Ulas Ayaz, Sertac Karaman

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).

Image Inpainting

Variational End-to-End Navigation and Localization

no code implementations25 Nov 2018 Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus

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

7 code implementations3 Oct 2018 Amado Antonini, Winter Guerra, Varun Murali, Thomas Sayre-McCord, Sertac Karaman

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

1 code implementation6 Jul 2018 Oscar Mickelin, Sertac Karaman

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

2 code implementations1 Jul 2018 Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman

Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving.

Autonomous Driving Depth Completion

Spatial Uncertainty Sampling for End-to-End Control

no code implementations13 May 2018 Alexander Amini, Ava Soleimany, Sertac Karaman, Daniela Rus

Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.

Autonomous Vehicles Bayesian Inference

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

6 code implementations21 Sep 2017 Fangchang Ma, Sertac Karaman

We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.

Depth Estimation Depth Prediction +2

CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization

4 code implementations2 May 2017 Corey Walsh, Sertac Karaman

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

1 code implementation4 Mar 2017 Fangchang Ma, Luca Carlone, Ulas Ayaz, Sertac Karaman

We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?

Compressive Sensing Depth Estimation +1

Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals

no code implementations26 Jul 2016 David Miculescu, Sertac Karaman

In this paper, we propose a coordination control algorithm for this problem, assuming stochastic models for the arrival times of the vehicles.

Autonomous Vehicles

Sampling-based Algorithms for Optimal Motion Planning

4 code implementations5 May 2011 Sertac Karaman, Emilio Frazzoli

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.


Incremental Sampling-based Algorithms for Optimal Motion Planning

3 code implementations3 May 2010 Sertac Karaman, Emilio Frazzoli

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

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