#149 Driver Assistance Using Automatic Understanding of the Driving Environment
- Principal Investigator
- Martial Hebert
- Status
- Completed
- Start Date
- Jan. 1, 2014
- End Date
- Dec. 31, 2014
- Project Type
- Research Advanced
- Grant Program
- MAP-21 TSET - Tier 1 (2012 - 2016)
- Grant Cycle
- 2014 TSET UTC
- Visibility
- Public
Abstract
An intelligent driving system must be cognizant of two basic elements to recommend effective and accurate actions: 1) the environment surrounding the vehicle, and 2) the expected movements of other objects. In this project we investigate advanced concepts to develop techniques for building internal models of the vehicle’s surrounding environment (including both static and dynamic objects), which can be used to assist drivers by warning them of risky or dangerous situations and recommending preventive actions. Our overall approach is to combine: 1) machine learning and scene understanding techniques with onboard sensors to classify and model the environment, 2) information from external data sources--such as maps--to boost the modeling accuracy and to provide additional contextual information, and 3) reinforcement learning techniques to model relationships between observed behavior and features in the environment, to predict the trajectories of moving objects.
Description
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Timeline
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Strategic Description / RD&T
Deployment Plan
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Expected Outcomes/Impacts
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Expected Outputs
TRID
Individuals Involved
| Name | Affiliation | Role | Position | |
|---|---|---|---|---|
| hebert@cs.cmu.edu | Hebert, Martial | Robotics Institute | PI | Faculty - Tenured |
| lenscmu@ri.cmu.edu | Navarro-Serment , Luis E. | Robotics Institute | Co-PI | Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$75000.00Total Project Budget (from all funding sources)
$75000.00Documents
| Type | Name | Uploaded |
|---|---|---|
| Final Report | Automatic_Recognition_and_Understanding_of_the_Driving_Environment_NA0uSjI.pdf | April 2, 2018, 5:06 a.m. |
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