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.


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.


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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