Multi-Hypothesis Multi-Model Driver's Gaze Target Tracking

For a safe handover of the driving task or driver-adaptive warning strategies the driver's situation awareness is a helpful source of information. In order to estimate and track the driver's focus of attention over time in a dynamic automotive scene, a Multi-Hypothesis Multi-Model probabilistic tracking framework was developed in which we postulate consistency between machine and human perception during gaze fixations. Within this framework, we explicitly included target object motion in the spatial transition step and integrated spatiotemporal models of human-like gaze behavior for fixations and saccades in the motion transition. This elaborate design makes the target estimation robust and yet flexible. At the same time, the representation in continuous 2D coordinates makes the algorithm run in real time on a standard laptop. By incorporating dynamic and static potential gaze targets from an object list and a free space spline, the algorithm is in principle independent from the applied sensor setup. The benefit of the proposed model is presented on real world data where the filter's tracking performance as well as the driver's visual sampling are presented based on an exemplary scene.

[1]  L. Petersson,et al.  A framework for driver-in-the-loop driver assistance systems , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[2]  Tobias Bär,et al.  Seen and missed traffic objects: A traffic object-specific awareness estimation , 2013, 2013 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops).

[3]  Michael Anthony Bauer,et al.  Detection and recognition of traffic signs inside the attentional visual field of drivers , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[4]  Mohan M. Trivedi,et al.  Attention estimation by simultaneous analysis of viewer and view , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[5]  Daniel Seifert,et al.  Traffic awareness driver assistance based on stereovision, eye-tracking, and head-up display , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[7]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[8]  Reinhard Klette,et al.  Vision-Based Driver-Assistance Systems , 2017 .

[9]  Marco Porta,et al.  A low-cost implementation of an eye tracking system for driver's gaze analysis , 2017, 2017 10th International Conference on Human System Interactions (HSI).

[10]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[11]  Volker Willert,et al.  Driver's gaze prediction in dynamic automotive scenes , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[12]  Christian A. Müller,et al.  Incorporating the Driver's Focus of Attention into Automotive Applications in Real Traffic and in Simulator Setups , 2016, 2016 12th International Conference on Intelligent Environments (IE).

[13]  Otto Lappi,et al.  Systematic Observation of an Expert Driver's Gaze Strategy—An On-Road Case Study , 2017, Front. Psychol..

[14]  Manuela M. Veloso,et al.  Multi-model motion tracking under multiple team member actuators , 2006, AAMAS '06.

[15]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Andrea Palazzi,et al.  DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[17]  Horst-Michael Groß,et al.  Combining behavior and situation information for reliably estimating multiple intentions , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[18]  Sujitha Martin,et al.  Object of Fixation Estimation by Joint Analysis of Gaze and Object Dynamics , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[19]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  Dariu Gavrila,et al.  Driver and pedestrian awareness-based collision risk analysis , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[21]  Qiang Ji,et al.  In the Eye of the Beholder: A Survey of Models for Eyes and Gaze , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Volker Willert,et al.  Compact Representation of Dynamic Driving Environments for ADAS by Parametric Free Space and Dynamic Object Maps , 2016, IEEE Transactions on Intelligent Transportation Systems.

[23]  Mohan Trivedi,et al.  Investigating the relationships between gaze patterns, dynamic vehicle surround analysis, and driver intentions , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[24]  Volker Willert,et al.  An Integrated Approach to Maneuver-Based Trajectory Prediction and Criticality Assessment in Arbitrary Road Environments , 2016, IEEE Transactions on Intelligent Transportation Systems.