112,271 research outputs found
Identification of test cases for Automated Driving Systems using Bayesian optimization
With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomous driving systems are highly safety critical in nature and need to be thoroughly tested for a diverse set of conditions before being commercially deployed. Due to the huge complexities involved with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traditional software testing methods have well-known limitations. They also fail to cover the infinite number of adverse conditions that can occur due to a slight change in the interactions between the environment and the system. Hence, it is important to identify test conditions that push the vehicle under test to breach its safe boundaries. Hazard Based Testing (HBT) methods, inspired by Systems-Theoretic Process Analysis (STPA), identify such parameterized test conditions that can lead to system failure. However, these techniques fall short of discovering the exact parameter values that lead to the failure condition. The presented paper proposes a test case identification technique using Bayesian Optimization. For a given test scenario, the proposed method learns parameter values by observing the system's output. The identified values create test cases that drive the system to violate its safe boundaries. STPA inspired outputs (parameters and pass/fail criteria) are used as inputs to the Bayesian Optimization model. The proposed method was applied to an SAE Level-4 Low Speed Automated Driving (LSAD) system which was modelled in a driving simulator
Comprehensive concept-phase system safety analysis for hybrid-electric vehicles utilizing automated driving functions
2019 Summer.Includes bibliographical references.Automotive system safety (SS) analysis involving automated driving functions (ADFs) and advanced driver assistance systems (ADAS) is an active subject of research but highly proprietary. A comprehensive SS analysis and a risk informed safety case (RISC) is required for all complex hybrid-vehicle builds especially when utilizing ADFs and ADAS. Industry standard SS procedures have been developed and are accessible but contain few detailed instructions or references for the process of completing a thorough automotive SS analysis. In this work, a comprehensive SS analysis is performed on an SAE-Level 2 autonomous hybrid-vehicle architecture in the concept phase which utilizes lateral and longitudinal automated corrective control actions. This paper first outlines a proposed SS process including a cross-functional SS working group procedure, followed by the development of an item definition inclusive of the ADFs and ADAS and an examination of 5 hazard analysis and risk assessment (HARA) techniques common to the automotive industry that were applied to 11 vehicle systems, and finally elicits the safety goals and functional requirements necessary for safe vehicle operation. The results detail functional failures, causes, effects, prevention, and mitigation methods as well as the utility of, and instruction for completing the various HARA techniques. The conclusion shows the resulting critical safety concerns for an SAE Level-2 autonomous system can be reduced through the use of the developed list of 116 safety goals and 950 functional safety requirements
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes
In the life cycle of highly automated systems operating in an open and
dynamic environment, the ability to adjust to emerging challenges is crucial.
For systems integrating data-driven AI-based components, rapid responses to
deployment issues require fast access to related data for testing and
reconfiguration. In the context of automated driving, this especially applies
to road obstacles that were not included in the training data, commonly
referred to as out-of-distribution (OoD) road obstacles. Given the availability
of large uncurated recordings of driving scenes, a pragmatic approach is to
query a database to retrieve similar scenarios featuring the same safety
concerns due to OoD road obstacles. In this work, we extend beyond identifying
OoD road obstacles in video streams and offer a comprehensive approach to
extract sequences of OoD road obstacles using text queries, thereby proposing a
way of curating a collection of OoD data for subsequent analysis. Our proposed
method leverages the recent advances in OoD segmentation and multi-modal
foundation models to identify and efficiently extract safety-relevant scenes
from unlabeled videos. We present a first approach for the novel task of
text-based OoD object retrieval, which addresses the question ''Have we ever
encountered this before?''.Comment: 11 pages, 7 figures, and 3 table
ΠΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΠ΅ Π²ΠΎΠΏΡΠΎΡΡ ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΉ Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ Π²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ
In the context of a fundamental change in the fundamental approaches to building a traffic management system, traditionally based on establishing the driverβs duty to ensure constant control over the traffic situation and, accordingly, presuming his responsibility for harm caused by a source of increased danger, the problem of legal regulation of the use of highly automated vehicles equipped with an automated driving system that does not provide for participation of the driver in the dynamic control of the car becomes not only relevant in theoretical, but also especially significant from practical aspects.The objective of the comprehensive study being conducted by the authors was to identify and visualise key groups of problems of legal regulation of the operation of cars with an automated driving system, to formulate proposals for their solution as part of a subsequent systemic legal study. This article is devoted to the results of consideration of the first block of the identified issues.Using the methods of the system-legal approach, formal-logical and formal-dogmatic analysis, the authors have identified the most problematic issues of legalising the terminology used in positive law and scientific sources. In particular, options for identifying the essential features of highly automated cars are proposed with the purpose to further legislatively determine the cars that should be classified as highly automated, to reveal which software and hardware complex can be considered an automated driving system, etc.Based on the results of solving the scientific problem, which consists in determining the directions for adapting the legislation governing the requirements for safety of vehicles and the procedure for their admission to operation for the needs of the widespread introduction of highly automated vehicles, the research can be carried out in two directions at the same time: to develop upper-level, essential requirements to safety and to develop specific, purely technical requirements for automated driving systems, as well as to develop a methodology for testing them.As a part of the taxonomic analysis carried out by the authors to determine, on a fundamentally new basis, the range of rights and obligations of the participants in the relations under the study, it is proposed to highlight the problem of distinguishing between situations in which the driver needs to take an active part in driving a car from situations in which he is not required to be actively involved. into this process.When considering issues of liability for harm caused by a car with an automated driving system, the article focuses on the need to solve the problem of balancing the responsibility of the car owner and the manufacturer, which can be facilitated by the application of methods of comparative legal analysis.Π ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΊΠΎΡΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈΠ°Π»ΡΠ½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΎΡΠΎΠΆΠ½ΠΎΠ³ΠΎ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ, ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΠΎ ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΠΎΠΉ Π½Π° ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠΈ ΠΎΠ±ΡΠ·Π°Π½Π½ΠΎΡΡΠΈ Π²ΠΎΠ΄ΠΈΡΠ΅Π»Ρ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ ΠΏΠΎΡΡΠΎΡΠ½Π½ΡΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π·Π° Π΄ΠΎΡΠΎΠΆΠ½ΠΎΠΉ ΠΎΠ±ΡΡΠ°Π½ΠΎΠ²ΠΊΠΎΠΉ ΠΈ, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ, ΠΏΡΠ΅Π·ΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π΅Π³ΠΎ ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ Π·Π° Π²ΡΠ΅Π΄, ΠΏΡΠΈΡΠΈΠ½ΡΠ½Π½ΡΠΉ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΠΎΠΉ ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ, ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π²ΡΡΠΎΠΊΠΎΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ², ΠΎΡΠ½Π°ΡΡΠ½Π½ΡΡ
Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ Π²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ, Π½Π΅ ΠΏΡΠ΅Π΄ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΠ΅ΠΉ ΡΡΠ°ΡΡΠΈΡ Π²ΠΎΠ΄ΠΈΡΠ΅Π»Ρ Π² Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΌ, ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π² ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ, Π½ΠΎ ΠΈ ΠΎΡΠΎΠ±ΠΎ Π·Π½Π°ΡΠΈΠΌΠΎΠΉ Π² ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΌ Π°ΡΠΏΠ΅ΠΊΡΠ°Ρ
.Π¦Π΅Π»ΡΠΌΠΈ Π²Π΅Π΄ΡΡΠ΅Π³ΠΎΡΡ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ°Π»ΠΈ Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ ΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΊΠ»ΡΡΠ΅Π²ΡΡ
Π³ΡΡΠΏΠΏ ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΉ Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ Π²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ, ΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅Π³ΠΎ ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎ-ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. Π Π΄Π°Π½Π½ΠΎΠΉ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΡ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ Π±Π»ΠΎΠΊΠ° ΠΎΠ±ΠΎΠ·Π½Π°ΡΠ΅Π½Π½ΡΡ
Π²ΠΎΠΏΡΠΎΡΠΎΠ².Π‘ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊ ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎ-ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°, ΡΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΡΠΎΡΠΌΠ°Π»ΡΠ½ΠΎ-Π΄ΠΎΠ³ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΠ΅ Π²ΠΎΠΏΡΠΎΡΡ Π»Π΅Π³Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠΎΠΉ Π² ΠΏΠΎΠ·ΠΈΡΠΈΠ²Π½ΠΎΠΌ ΠΏΡΠ°Π²Π΅ ΠΈ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°Ρ
. Π ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ Π²Π°ΡΠΈΠ°Π½ΡΡ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΡΡΠ½ΠΎΡΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π²ΡΡΠΎΠΊΠΎΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΉ Π² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°Ρ
ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅Π³ΠΎ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΎΠ³ΠΎ, ΠΊΠ°ΠΊΠΈΠ΅ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»ΠΈ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΠΎΡΠ½Π΅ΡΠ΅Π½Ρ ΠΊ Π²ΡΡΠΎΠΊΠΎΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ, ΠΊΠ°ΠΊΠΎΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-Π°ΠΏΠΏΠ°ΡΠ°ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡ ΠΌΠΎΠΆΠ΅Ρ ΡΡΠΈΡΠ°ΡΡΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ Π²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΈ Ρ.ΠΏ.ΠΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π½Π°ΡΡΠ½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ, ΡΠΎΡΡΠΎΡΡΠ΅ΠΉ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π°, ΡΠ΅Π³ΡΠ»ΠΈΡΡΡΡΠ΅Π³ΠΎ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ ΠΊ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ² ΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΈΡ
Π΄ΠΎΠΏΡΡΠΊΠ° ΠΊ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΈ ΠΊ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΡΠΌ ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ Π²ΡΡΠΎΠΊΠΎΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΉ, Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΡΠ΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄, ΡΡΠΎ Π΄Π°Π½Π½Π°Ρ ΡΠ°Π±ΠΎΡΠ° ΠΌΠΎΠΆΠ΅Ρ Π²Π΅ΡΡΠΈΡΡ Π² Π΄Π²ΡΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎ: ΠΏΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π²Π΅ΡΡ
Π½Π΅ΡΡΠΎΠ²Π½Π΅Π²ΡΡ
, ΡΡΡΠ½ΠΎΡΡΠ½ΡΡ
ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΠΉ ΠΊ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΏΠΎ Π²ΡΡΠ°Π±ΠΎΡΠΊΠ΅ ΡΠΎΡΠ΅ΡΠ½ΡΡ
, ΡΡΠ³ΡΠ±ΠΎ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΠΉ ΠΊ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌ Π²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈΡ
ΠΈΡΠΏΡΡΠ°Π½ΠΈΠΉ.Π ΡΠ°ΠΌΠΊΠ°Ρ
ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠ°ΠΊΡΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ½Π½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ Π² ΡΠ΅Π»ΡΡ
ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΊΡΡΠ³Π° ΠΏΡΠ°Π² ΠΈ ΠΎΠ±ΡΠ·Π°Π½Π½ΠΎΡΡΠ΅ΠΉ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΎΡΠΎΠ±ΠΎ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ°Π·Π³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΉ, Π² ΠΊΠΎΡΠΎΡΡΡ
Π²ΠΎΠ΄ΠΈΡΠ΅Π»Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ΅ ΡΡΠ°ΡΡΠΈΠ΅ Π² ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΌ, ΠΎΡ ΡΠΈΡΡΠ°ΡΠΈΠΉ, Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΎΡ Π½Π΅Π³ΠΎ Π½Π΅ ΡΡΠ΅Π±ΡΠ΅ΡΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π²ΠΎΠ²Π»Π΅ΡΠ΅Π½ΠΈΡ Π² ΡΡΠΎΡ ΠΏΡΠΎΡΠ΅ΡΡ.ΠΡΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠΈ Π²ΠΎΠΏΡΠΎΡΠΎΠ² ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ Π·Π° Π²ΡΠ΅Π΄, ΠΏΡΠΈΡΠΈΠ½ΡΠ½Π½ΡΠΉ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΠΌ Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠΎΠΉ Π²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ, Π΄Π΅Π»Π°Π΅ΡΡΡ Π°ΠΊΡΠ΅Π½Ρ Π½Π° Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ Π±Π°Π»Π°Π½ΡΠ° ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ Π²Π»Π°Π΄Π΅Π»ΡΡΠ° Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Ρ ΠΈ ΠΈΠ·Π³ΠΎΡΠΎΠ²ΠΈΡΠ΅Π»Ρ, ΡΠ΅ΠΌΡ ΠΌΠΎΠΆΠ΅Ρ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΠΎΠ²Π°ΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΊΠΎΠΌΠΏΠ°ΡΠ°ΡΠΈΠ²Π½ΠΎ-ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Scenario-based testing for the safety validation of highly automated vehicles
is a promising approach that is being examined in research and industry. This
approach heavily relies on data from real-world scenarios to derive the
necessary scenario information for testing. Measurement data should be
collected at a reasonable effort, contain naturalistic behavior of road users
and include all data relevant for a description of the identified scenarios in
sufficient quality. However, the current measurement methods fail to meet at
least one of the requirements. Thus, we propose a novel method to measure data
from an aerial perspective for scenario-based validation fulfilling the
mentioned requirements. Furthermore, we provide a large-scale naturalistic
vehicle trajectory dataset from German highways called highD. We evaluate the
data in terms of quantity, variety and contained scenarios. Our dataset
consists of 16.5 hours of measurements from six locations with 110 000
vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane
changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems
(ITSC) 201
Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges
The technology in the area of automated vehicles is gaining speed and
promises many advantages. However, with the recent introduction of
conditionally automated driving, we have also seen accidents. Test protocols
for both, conditionally automated (e.g., on highways) and automated vehicles do
not exist yet and leave researchers and practitioners with different
challenges. For instance, current test procedures do not suffice for fully
automated vehicles, which are supposed to be completely in charge for the
driving task and have no driver as a back up. This paper presents current
challenges of testing the functionality and safety of automated vehicles
derived from conducting focus groups and interviews with 26 participants from
five countries having a background related to testing automotive safety-related
topics.We provide an overview of the state-of-practice of testing active safety
features as well as challenges that needs to be addressed in the future to
ensure safety for automated vehicles. The major challenges identified through
the interviews and focus groups, enriched by literature on this topic are
related to 1) virtual testing and simulation, 2) safety, reliability, and
quality, 3) sensors and sensor models, 4) required scenario complexity and
amount of test cases, and 5) handover of responsibility between the driver and
the vehicle.Comment: 8 page
- β¦