SafeVRU

A research platform for the interaction of self-driving vehicles with vulnerable road users

Conference paper (2019)

Authors

L. Ferranti Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
B.F. Ferreira de Brito Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
E.A.I. Pool Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Y. Zheng Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
R.M. Ensing Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
R. Happee Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
B. Shyrokau Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
J.F.P. Kooij Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
D. Gavrila Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2019 L. Ferranti, B.F. Ferreira de Brito, E.A.I. Pool, Y. Zheng, R.M. Ensing, R. Happee, B. Shyrokau, J.F.P. Kooij, J. Alonso-Mora, D. Gavrila
DOI
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https://doi.org/10.1109/IVS.2019.8813899
More Info
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

This paper presents our research platform SafeVRU for the interaction of self-driving vehicles with Vulnerable Road Users (VRUs, i.e., pedestrians and cyclists). The paper details the design (implemented with a modular structure within ROS) of the full stack of vehicle localization, environment perception, motion planning, and control, with emphasis on the environment perception and planning modules. The environment perception detects the VRUs using a stereo camera and predicts their paths with Dynamic Bayesian Networks (DBNs), which can account for switching dynamics. The motion planner is based on model predictive contouring control (MPCC) and takes into account vehicle dynamics, control objectives (e.g., desired speed), and perceived environment (i.e., the predicted VRU paths with behavioral uncertainties) over a certain time horizon. We present simulation and real-world results to illustrate the ability of our vehicle to plan and execute collision-free trajectories in the presence of VRUs.

Files

08813899.pdf
(.pdf | 3.45 Mb)