Pedestrian path prediction using body language traits

Abstract

Driver Assistance Systems have achieved a high level of maturity in the latest years. As an example of that, sophisticated pedestrian protection systems are already available in a number of commercial vehicles from several OEMs. However, accurate pedestrian path prediction is needed in order to go a step further in terms of safety and reliability, since it can make the difference between effective and non-effective intervention. In this paper, we consider the three-dimensional pedestrian body language in order to perform path prediction in a probabilistic framework. For this purpose, the different body parts and joints are detected using stereo vision. We propose the use of GPDM (Gaussian Process Dynamical Models) for reducing the high dimensionality of the input feature vector (composed by joints and displacement vectors) in the 3D pose space and for learning the pedestrian dynamics in a latent space. Experimental results show that accurate path prediction can be achieved at a time horizon of ≈ 0.8 s.

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

“…The Bayesian Network used multivariate Gaussian distributions for the relative position and velocity with respect to the vehicle for a predicted time. There has also been research undertaken using body features that can aid in predicting a pedestrian's future behaviour such as walking, standing, bending, jogging and running [188,209,210]. The head orientation of the pedestrian was considered in [183,203].…”

Human motion trajectory prediction: a survey

“…motion state such as position and possibly velocity (e.g., Bennewitz et al, 2005; Bera et al, 2016; Elfring et al, 2014; Ferrer and Sanfeliu, 2014; Karasev et al, 2016; Kitani et al, 2012; Kooij et al, 2019; Kucner et al, 2017; Kuderer et al, 2012; Pellegrini et al, 2009; Trautman and Krause, 2010; Ziebart et al, 2009); 1.2. articulated pose such as head orientation (e.g., Hasan et al, 2018; Kooij et al, 2019, 2014; Roth et al, 2016; Unhelkar et al, 2015) or fullbody pose (Mínguez et al, 2018; Quintero et al, 2014); 1.3. semantic attributes such as the age and gender (Ma et al, 2017), personality (Bera et al, 2017), and awareness of the robot’s presence (Kooij et al, 2019; Oli et al, 2013).…”

“…1.2. articulated pose such as head orientation (e.g., Hasan et al, 2018; Kooij et al, 2019, 2014; Roth et al, 2016; Unhelkar et al, 2015) or fullbody pose (Mínguez et al, 2018; Quintero et al, 2014);…”

Composable Q- Functions for Pedestrian Car Interactions

“…In particular, [3] address this problem by adding pedestrian's head orientation as an intention feature to assess the criticality of situations. Other features that may be used are the human pose [17], [18] and optical flow [16] to determine the state of a pedestrian.…”