Technical Paper collections have been re-named for better clarity and alignment.x

Your Selections

General Motors LLC, USA
Show Only

Collections

File Formats

Content Types

Dates

Sectors

Topics

Authors

Publishers

Affiliations

   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

A Maneuver-Based Threat Assessment Strategy for Collision Avoidance

SAE International Journal of Passenger Cars - Electronic and Electrical Systems

General Motors LLC, USA-Jinsong Wang
Beihang University, China-Weiwen Deng
  • Journal Article
  • 07-12-01-0003
Published 2019-08-22 by SAE International in United States
Advanced driver-assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of the driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This article presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modelled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models. Subsequently, time to collision (TTC) is calculated in a strategy of employing collision detection at every predicted trajectory instance. For this purpose, safe areas via bounding boxes are applied on every vehicle, and Separating Axis Theorem (SAT) is applied for collision prediction…
Annotation ability available