Virtual test drives with multiple vehicles under test for the evaluation of collaborative assisted and automated driving functions

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.

Download Full-text

Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities

Sensors ◽

10.3390/s20113309 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3309 ◽

Cited By ~ 1

Author(s):

Stefan Muckenhuber ◽

Hannes Holzer ◽

Zrinka Bockaj

Keyword(s):

Infrared Spectrum ◽

Material Properties ◽

Incidence Angle ◽

Lidar Measurement ◽

Automated Driving ◽

Measurement Device ◽

Test Environment ◽

Physical Test ◽

Virtual Test ◽

Modelling Approach

Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach that takes material properties and corresponding lidar capabilities into account. The considered material property is the incidence angle dependent reflectance of the illuminated material in the infrared spectrum and the considered lidar property its capability to detect a material with a certain reflectance up to a certain range. A new material classification for lidar modelling in the automotive context is suggested, distinguishing between 7 material classes and 23 subclasses. To measure angle dependent reflectance in the infrared spectrum, a new measurement device based on a time of flight camera is introduced and calibrated using Lambertian targets with defined reflectance values at 10 % , 50 % , and 95 % . Reflectance measurements of 9 material subclasses are presented and 488 spectra from the NASA ECOSTRESS library are considered to evaluate the new measurement device. The parametrisation of the lidar capabilities is illustrated by presenting a lidar measurement campaign with a new Infineon lidar prototype and relevant data from 12 common lidar types.

Download Full-text

Virtual Test of Automated Driving Functions

ATZ worldwide ◽

10.1007/s38311-020-0228-7 ◽

2020 ◽

Vol 122 (5) ◽

pp. 16-21

Author(s):

Torsten Butz ◽

Simon Paleduhn ◽

Alexander Merkel ◽

Christian Bohner

Keyword(s):

Automated Driving ◽

Virtual Test

Download Full-text

Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks

Proceedings - Fahrerassistenzsysteme 2018 ◽

10.1007/978-3-658-23751-6_18 ◽

2019 ◽

pp. 203-211 ◽

Cited By ~ 3

Author(s):

Raphael Pfeffer ◽

Patrick Ukas ◽

Eric Sax

Keyword(s):

Neural Networks ◽

Automated Driving ◽

Virtual Test ◽

Highly Automated Driving

Download Full-text

Cooperative Intelligent Transport Systems: Towards high-level automated driving

10.1049/pbtr025e ◽

2019 ◽

Keyword(s):

Transport Systems ◽

Automated Driving ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

High Level

Download Full-text

Quantitative Evaluation of Attention to the Driving Environment During Automated Driving at Levels 3 using ERP: Evaluation of Psychophysiological State of Driver

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.138.1148 ◽

2018 ◽

Vol 138 (9) ◽

pp. 1148-1153 ◽

Cited By ~ 2

Author(s):

Tomohiro Murase ◽

Kosuke Oiwa ◽

Akio Nozawa

Keyword(s):

Quantitative Evaluation ◽

Automated Driving ◽

Psychophysiological State

Download Full-text

In-Plane Flexible Ring Tire Model—Part 1: Modeling and Parameter Identification

Tire Science and Technology ◽

10.2346/tire.18.460303 ◽

2018 ◽

Vol 46 (3) ◽

pp. 174-219 ◽

Cited By ~ 2

Author(s):

Bin Li ◽

Xiaobo Yang ◽

James Yang ◽

Yunqing Zhang ◽

Zeyu Ma

Keyword(s):

Static Load ◽

Model Parameters ◽

Tire Model ◽

Test Results ◽

Lumped Mass ◽

Virtual Test ◽

Proposed Model ◽

Particle Swarm Method ◽

Vehicle Dynamic Simulation ◽

Flexible Ring

ABSTRACT The tire model is essential for accurate and efficient vehicle dynamic simulation. In this article, an in-plane flexible ring tire model is proposed, in which the tire is composed of a rigid rim, a number of discretized lumped mass belt points, and numerous massless tread blocks attached on the belt. One set of tire model parameters is identified by approaching the predicted results with ADAMS® FTire virtual test results for one particular cleat test through the particle swarm method using MATLAB®. Based on the identified parameters, the tire model is further validated by comparing the predicted results with FTire for the static load-deflection tests and other cleat tests. Finally, several important aspects regarding the proposed model are discussed.

Download Full-text

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

Transport and Communication Science Journal ◽

10.25073/tcsj.70.3.7 ◽

2019 ◽

Vol 70 (3) ◽

pp. 214-224

Author(s):

Bui Ngoc Dung ◽

Manh Dzung Lai ◽

Tran Vu Hieu ◽

Nguyen Binh T. H.

Keyword(s):

Machine Learning ◽

Gaussian Mixture ◽

Research Field ◽

Transport Systems ◽

Learning Approaches ◽

Subtraction Method ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Detection And Tracking ◽

Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Download Full-text