FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
Paper in proceeding, 2020

Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.

skeletal fitting

fusion models

densenet

Pedestrian intention

bounding box

Author

Francesco Piccoli

University of California

Rajarathnam Balakrishnan

University of California

Maria Jesus Perez

University of California

Moraldeepsingh Sachdeo

University of California

Carlos Nunez

University of California

Matthew Tang

Student at Chalmers

Kajsa Andreasson

Student at Chalmers

Kalle Bjurek

Student at Chalmers

Ria Dass Raj

Student at Chalmers

Ebba Davidsson

Student at Chalmers

Colin Eriksson

Student at Chalmers

Victor Hagman

Student at Chalmers

Jonas Sjöberg

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Ying Li

Volvo Cars

L. Srikar Muppirisetty

Volvo Cars

Sohini Roychowdhury

Volvo Cars

Conference Record - Asilomar Conference on Signals, Systems and Computers

10586393 (ISSN)

Vol. 2020-November 68-72 9443552

54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Pacific Grove, USA,

Subject Categories

Computer Engineering

Computer Systems

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/IEEECONF51394.2020.9443552

More information

Latest update

6/24/2021