About Us

About Us ​Honda Research Institute, USA was established in 2003 as North America's advanced research center that provides innovative solutions to complex problems with direct applications to Honda's current and future technology roadmap​.  Our team of scientists and engineers create technologies, ofte​n generated through a scientific process, and apply them to real situations, addressing more than just abstract principles.​​​  Our core principles include: (1) Maintaining a commitment to high quality and innovative research that supports Honda's short and long term strategy.​​ (2) Fostering an open innovation model that establishes partnerships and alliances with academia and the private sector. HRI-US Research Centers​​ Our offices are located San Jose California and Columbus Ohio.    The San Jose research center focuses on computer science and related research​ themes, including autonomous systems, human machine interaction, and computer vision.   Material science related research is conducted in the Columbus research center on the campus of The Ohio State University. The research themes in the Columbus research center include advanced material analysis, advanced batteries, nanomaterials for energy applications, and synthesis and studies of nanomaterials. Honda Innovations (Honda R&D Innovations, Inc.) Co-located with HR​I-US is Honda Innovations (Honda R&D Innovations, Inc.), another Honda R&D com pany .  Honda Innovations drives transformative collaboration within Honda – Leading open innovation initiatives globally, Honda Innovations partners with innovators of all shapes and sizes from startups to global brands. Focus areas include: Connected Vehicle/ IoT , Human Machine Interface, Machine Intelligence/Robotics, Personal Mobility, Sharing Economy, Connected Services, Clean Energy, and Industrial Innovation. ​For more information about Honda Innovations, please visit   http:// www.hondainnovations.com / .​         San Jose Research Center 70 Rio Robles San Jose, CA 95134 HRI_contact@honda-ri.com ​ ​​ ​   Columbus Research Center 1381 Kinnear Rd., Suite 116 Columbus, OH 43212 Phone: 614-340-6081   Fax: 614-340-6082​​​​ HRI_contact@honda-ri.com ​​       WeiterlesenÜberAbout Us »

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Under Construction

HAD

Introduction - H3D - Introductory Text

The H3D is a large scale full-surround 3D multi-object detection and tracking dataset. It is gathered from HDD dataset, a large scale naturalistic driving dataset collected in San Francisco Bay Area. WeiterlesenÜberIntroduction - H3D - Introductory Text »

Downloads - H3D Landing Page

This dataset corresponds to the paper,  "The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes" , as it appears in ICRA 2019. In the current release, the data is available for universities in the US. To obtain the dataset, please follow the following instructions. Important:  The following instructions must be followed exactly: Please use your official university email to  ychen@honda-ri.com  to request the agreement. You will receive a link and password to the data in your official email address after the review. Please cite the following paper if you find the dataset useful in your work: @inproceedings{360LiDARTracking_ICRA_2019,     author = {Abhishek Patil and Srikanth Malla and Haiming Gang and Yi-Ting Chen},     title = {The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes},       booktitle = {International Conference on Robotics and Automation},     year = {2019} } WeiterlesenÜberDownloads - H3D Landing Page »

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H3D

README.md

Introduction

Introduction - HDD - Introductory Text

HDD Introduction The videos below provide examples of Goal-oriented driving behavior from the dataset. GPS coordinates together with sensor values from the CAN bus are shown on top of the front-facing camera stream. Videos WeiterlesenÜberIntroduction - HDD - Introductory Text »

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Introduction

RE: I want access to this data set

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I want access to this data set

Associate Positions Introduction

Honda Research Institute USA (HRI-US) is at the cutting edge of Honda's research and development activities. Inspired by Honda's global slogan - The Power of Dreams - we pursue emerging technologies and bring them into reality to make people happy, even as we are engaged daily in highly scientific, pioneering work. We realize that dreams don't co me from organizations, systems, or money. They come from people, and we seek people who have such challenging spirits to work with us. WeiterlesenÜberAssociate Positions Introduction »

Downloads Landing Page

Downloads - HDD Landing Page

This dataset corresponds to the paper, "Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning" , as it appears in CVPR 2018. In the current release, the data is available for universities in the US. To obtain the dataset, please follow the following instructions. Important:  The following instructions must be followed exactly: Please use your official university email to ychen@honda-ri.com  to request the agreement. You will receive a link and password to the data in your official email address after the review. Please cite the following paper if you find the dataset useful in your work: @inproceedings{Ramanishka_behavior_CVPR_2018,     author = {Vasili Ramanishka and Yi-Ting Chen and Teruhisa Misu and Kate Saenko},     title = {Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning},       booktitle = {Conference on Computer Vision and Pattern Recognition},     year = {2018} } WeiterlesenÜberDownloads - HDD Landing Page »

Human Behavior/State Understanding

The title includes multiple positions which focus on developing computer vision, signal processing, and machine learning algorithms for human-vehicle interactions in traffic scenes. WeiterlesenÜberHuman Behavior/State Understanding »

Projects

We compute various musculoskeletal indicators of human performance when the driver is operating a vehicle under normal and emergency maneuvering.
The goal of the project is to achieve robust lane-level localization for cars using low cost mass producible sensors.
We have developed online algorithms to transfer motion from a human demonstrator to Honda's humanoid robot, ASIMO.
The development of the next generation green and safe batteries with high energy density is highly desirable for meeting the rapidly growing needs of electrical vehicles.
Rare earth Manganese Oxide (ReMO) as a cathode material has potential capacity higher than that of commercial lithium manganese oxide.
Metal-air batteries are solid state batteries using metal oxidation at the anode and oxygen reduction at the cathode to induce a current flow.
This project is focused on CNT and graphene enforced electrode materials for secondary battery and supercapacitor applications.
This research is focused on scale-up technology for continuous synthesis of SWNTs by CVD method and the exploration of their performance in actual electrochemical devices
The core of this project is for atomistic level understanding of environmental impact on conductivities of SWNTs and 2-D materials in order to reveal their ultimate sensitivities.
Synthesis and studies of growth mechanism, self assembly and properties of low dimensional nanomaterials for alternative energy technologies are at the core of our research.
The goal is to develop driving aids that enhance the driver's situational awareness and give drivers a sense of confidence and trust in the vehicles they are operating.
In Knowledge Discovery, we Integrate knowledge from multiple sources such as Wikipedia, Yahoo Question/Answers, Open Directory Project and OpenMind.
Probabilistic model to track dialog state and provide information to driver viaspoken dialog
This project presents a control theoretic approach for human pose estimation from a set of key feature points detected using depth image streams obtained from a time of flight imaging device.
We are developing a real-time system that detects and tracks traffic participants.
We aim to develop a robust pedestrian detection algorithm that can handle partial occlussions.
We are taking advantage of our autonomous driving platform to create a comprehensive repository of annotated sensor data that provide computer vision benchmarks and training data to support advanced driving assist and autonomous driving applications.
Backing-up of articulated vehicles poses a difficult challenge even for experienced drivers. While long wheelbase dual-axle trailers provide a benefit of increased capacity over their single-axle counterparts, backing-up of such systems is especially difficult. We devise a control strategy for such systems, allowing backing-up maneuvers to be intuitive to drivers without experience with trailers. Using hitch angle feedback, we show these concepts can be used to stabilize the trailer in back-up motion in the presence of arbitrary driver inputs.
Utilizing the latest wearable sensing technologies and patented motion prediction algorithms, the goal is to predict human movement and perform biomechanical computations based on those predictions.