Software Framework for Testing of Automated Driving Systems in a Dynamic Traffic Environment
November 09, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Demin Nalic, Aleksa Pandurevic, Arno Eichberger, Branko Rogic
arXiv ID
2011.05798
Category
cs.SE: Software Engineering
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Virtual testing of automated driving systems (ADS) has become an essential part of testing procedures for all automation levels. As ADS from automation level 3 and up are very complex, virtual testing for such systems is inevitable. The complexity of these levels lies in the modelling and calculation demand for the virtual environment which consists of roads, traffic, static and dynamic objects as well as the modelling of the car itself. For safety and performance analyses of ADS, the most important part is the modelling and consideration of road traffic participants. There is multiple traffic flow simulation software (TFSS) which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software (VSS). For these software environments, the possibility to manipulate traffic participants in a defined manner e.g. in the vicinity of the vehicle under test or implementing defined driver models for traffic vehicles is beneficial. In this paper, we present a software framework based on the external driver model interface provided by Vissim. This framework makes it possible to easily manipulate traffic participants for testing purposes of ADS.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted