Accession Number:

ADA621246

Title:

Know Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Descriptive Note:

Conference paper preprint

Corporate Author:

CORNELL UNIV ITHACA NY DEPT OF COMPUTER SCIENCE

Report Date:

2015-04-01

Pagination or Media Count:

11.0

Abstract:

Advanced Driver Assistance Systems ADAS have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers a few seconds beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. Anticipation requires modeling the drivers action space, events inside the vehicle such as their head movements, and also the outside environment. Performing this joint modeling makes anticipation a challenging problem. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the context from both inside and outside of the car. We represent the context with expressive features and propose an Autoregressive Input-Output HMM to model the contextual information. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80 F1-score. Our computation time during inference is under 3.6 milliseconds.

Subject Categories:

  • Cybernetics
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE