Prediction and Behaviors for Driver Assistance and Socially Cooperative Autonomous Driving

Khurana, Aman; Dong, Chiyu; Zhang, Yihuan; Dolan, John M

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Prediction and Behaviors for Driver Assistance and Socially Cooperative Autonomous Driving

2018-10-31
By Khurana, Aman ; Dong, Chiyu ; Zhang, Yihuan

File Language:

English

Details

Creators:

Khurana, Aman ; Dong, Chiyu ; Zhang, Yihuan ; Dolan, John M
Corporate Creators:

Technologies for Safe and Efficient Transportation. University Transportation Center ; Robotics Institute, Carnegie Mellon University
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

Technologies for Safe and Efficient Transportation. University Transportation Center
Abstract:

The goal of this project was to develop better prediction and behaviors generation methods with application to driver assistance and autonomous driving. Prediction of the behavior of other cars in traffic allows both driver assistance and autonomous driving systems to better plan their immediate future behavior. The resultant behaviors can then be more “socially cooperative”, i.e., they can increase the predictability, fluidity, and safety of inter-car interactions. This report presents work on two different scenarios: 1) roundabouts or traffic circles, in which drivers need to gauge the behaviors of cars in the traffic circle in order to enter and exit safely; 2) lane change situations, in which prediction of whether surrounding cars will change lanes and if so, what their likely trajectory will be, can improve the smoothness and safety of egovehicle motion. The report first presents the roundabout/traffic circle work, then appends a conference paper published at IROS (IEEE International Conference on Robots and Systems) 2017 summarizing the lane change prediction work.
Format:

PDF
Funding:

DTRT12GUTG11
Download URL:

https://rosap.ntl.bts.gov/view/dot/63595/dot_63595_DS1.pdf
File Type:

[PDF - 1.59 MB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha256:bd532c78a1f9fb5f3458a5cccd11d597c0e43f3ef486d2ef57e1cfe6f53edc45

File Language:

English

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