Virtual Road Environment Research Articles

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Parameterized Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles: A Real-Time Dynamic Programming Approach

This paper presents a vehicle speed planning system called the energy-optimal deceleration planning system (EDPS), which aims to maximize energy recuperation of regenerative braking of connected and autonomous electrified vehicles. A recuperation energy-optimal speed profile is computed based on the impending deceleration requirements for turning or stopping at an intersection. This is computed to maximize the regenerative braking energy while satisfying the physical limits of an electrified powertrain. In automated driving, the powertrain of an electrified vehicle can be directly controlled by the vehicle control unit such that it follows the computed optimal speed profile. To obtain smooth optimal deceleration speed profiles, optimal deceleration commands are determined by a parameterized polynomial-based deceleration model that is obtained by regression analyses with real vehicle driving test data. The parameters are dependent on preview information such as residual time and distance as well as target speed. The key design parameter is deceleration time, which determines the deceleration speed profile to satisfy the residual time and distance constraints as well as the target speed requirement. The bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. The state constraints are dynamically updated by considering the anticipated road load and the deceleration preference. For validation and comparisons of the EDPS with different preview distances, driving simulation tests with a virtual road environment and vehicle-to-infrastructure connectivity are presented. It is shown that the longer preview distance in the EDPS, the more energy-recuperation. In comparison with driver-in-the-loop simulation tests, EDPS-based autonomous driving shows improvements in energy recuperation and reduction in trip time.

AR を用いた交通安全教育のための仮想道路環境の構築 ～安定した仮想道路表示のためのシステム設計～

AR を用いた交通安全教育のための仮想道路環境の構築 ～安定した仮想道路表示のためのシステム設計～

Testing Autonomous Vehicle Software in the Virtual Prototyping Environment

Modern vehicle is equipped with autonomous features, such as precollision system or adaptive cruise control to help people perform driving in a safer and more convenient way. The software complexity of those autonomous features is growing to accommodate various needs from users, which makes it more difficult to test their correctness. Virtual prototyping allows one to test the vehicle software in the virtual road environment. Even though several tools are available, original equipment manufacturers seem to be hesitating to fully integrate the virtual test as a part of the vehicle development plan. One of the obstacle is due to a lack of well-defined test criteria that can reasonably abstract the physical environment and test case generation methods that automatically visualize the virtual road environment. In this letter, we propose several associated research directions and potential approaches from the perspective of test criteria and test case generation.

A computational model of the car driver interfaced with a simulation platform for future Virtual Human Centred Design applications: COSMO-SIVIC

This paper presents the first step of a research programme implemented by IFSTTAR in order to develop an integrative simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This virtual platform, named COSMO-SiVIC, implements a COgnitive Simulation MOdel of the DRIVEr (i.e. COSMODRIVE) into a Vehicle–Environment–Sensors platform (named SiVIC, for Simulateur Véhicule-Infrastructure-Capteur). From this simulation tool based on a computational driver model, the design costs of driving assistances is expected to reduce in the future, and the end-users needs during the design process are also better taken into account. This article is mainly focussed on the description of the driver model developed and implemented on the SiVIC virtual platform, which is only the first step towards a future Virtual HCD integrated tool. The first section will discuss the research context and objective, and the second one will present the theoretical background in cognitive sciences supporting our driver modelling approach. Then, the SiVIC tool is used in this research as a methodological and technical support for both empirical data collection among human drivers and as a virtual road environment to be interfaced with the COSMODRIVE model. In the result section, the functional architecture of COSMO-SIVIC (based on three complementary modules of Perception, Decision and Action) will be described, and an example of virtual simulation of human driver's errors due to visual distraction while driving will be presented. The perspectives concerning future use of COSMO-SIVIC for virtual HCD will be then discussed in the conclusion section.

Simulation of General 3D Virtual Stochastic Road Model

In order to provide the research of virtual prototype a complex 3-D virtual road environment based on a new method，a 3-D Pseudo Excitation Method stochastic road model was established based on the single-point Pseudo Excitation Method model, and then the simulation result was gained by Matlab.What’s more，based on the theory of triangle network，a general 3-D virtual road model with the arithmetic of nodes and elements was created.Then a specified 3-D virtual road file was imported into Adams to form the 3-D virtua1 road. Finally, a C-level virtual road was generated for the riding performance simulation of virtual vehicle.By comparing the simulation result with the GB／T 13441-92，the virtual vehicle’s reasonable time under the ride performance requirement was obtained.