Search Results for author: Eshed Ohn-Bar

Found 12 papers, 2 papers with code

SelfD: Self-Learning Large-Scale Driving Policies From the Web

no code implementations21 Apr 2022 Jimuyang Zhang, Ruizhao Zhu, Eshed Ohn-Bar

However, it is difficult to directly leverage such large amounts of unlabeled and highly diverse data for complex 3D reasoning and planning tasks.

Data Augmentation Decision Making +1

Learning by Watching

no code implementations CVPR 2021 Jimuyang Zhang, Eshed Ohn-Bar

When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed.

X-World: Accessibility, Vision, and Autonomy Meet

no code implementations ICCV 2021 Jimuyang Zhang, Minglan Zheng, Matthew Boyd, Eshed Ohn-Bar

We tackle inherent data scarcity by leveraging a simulation environment to spawn dynamic agents with various mobility aids.

Instance Segmentation Semantic Segmentation

Learning Situational Driving

no code implementations CVPR 2020 Eshed Ohn-Bar, Aditya Prakash, Aseem Behl, Kashyap Chitta, Andreas Geiger

Motivated by this observation, we develop a framework for learning a situational driving policy that effectively captures reasoning under varying types of scenarios.

Label Efficient Visual Abstractions for Autonomous Driving

3 code implementations20 May 2020 Aseem Behl, Kashyap Chitta, Aditya Prakash, Eshed Ohn-Bar, Andreas Geiger

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.

Autonomous Driving Semantic Segmentation

Future Near-Collision Prediction from Monocular Video: Feasibility, Dataset, and Challenges

1 code implementation21 Mar 2019 Aashi Manglik, Xinshuo Weng, Eshed Ohn-Bar, Kris M. Kitani

Our results show that our proposed multi-stream CNN is the best model for predicting time to near-collision.

Robotics

Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning

no code implementations22 Jun 2018 Xinlei Pan, Eshed Ohn-Bar, Nicholas Rhinehart, Yan Xu, Yilin Shen, Kris M. Kitani

The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states.

reinforcement-learning

Personalized Dynamics Models for Adaptive Assistive Navigation Systems

no code implementations11 Apr 2018 Eshed Ohn-Bar, Kris Kitani, Chieko Asakawa

Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination.

Model-based Reinforcement Learning Transfer Learning

Driver Hand Localization and Grasp Analysis: A Vision-based Real-time Approach

no code implementations22 Feb 2018 Siddharth, Akshay Rangesh, Eshed Ohn-Bar, Mohan M. Trivedi

This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand.

Hand Detection

Multi-scale Volumes for Deep Object Detection and Localization

no code implementations14 May 2015 Eshed Ohn-Bar, M. M. Trivedi

This study aims to analyze the benefits of improved multi-scale reasoning for object detection and localization with deep convolutional neural networks.

Object Detection

Learning to Detect Vehicles by Clustering Appearance Patterns

no code implementations12 Mar 2015 Eshed Ohn-Bar, Mohan M. Trivedi

This paper studies efficient means for dealing with intra-category diversity in object detection.

Object Detection

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