在⾃动驾驶仿真领域，场景⼀直是⼤家关注的重中之重。因为⾃动驾驶仿真与传统的仿真的最⼤区别就是缺少了⼈。⾯对复杂的交通状况做出灵活应对，在⾃动驾驶之前⼀直是⼈类驾驶员的责任，⽽在⾃动驾驶⾯世之后，车辆本⾝需要对⾃⼰负责、对乘坐在其上的乘客负责。因此，需要在⾃动驾驶车辆上路之前，尽可能多的经历不同场景的测试和考验，才能让消费者放⼼，让⼚商⾃⼰安⼼。

AD requires a multifaceted process incorporating a variety of software tools

But none of these tools were ever designed to work together

This costs the industry time and money

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We are mitigating this problem by creating the leading automated driving ecosystem ➢ OpenADx

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Common Datta Prrobllems

• Increasing volumes of data.

• Spending too much time looking for the correct data or manually distributing data.

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• Repeating tests because data cannot be found or re-used with confidence.

• Limited or inconsistent electronic description of the data.

• Many different proprietary data formats causing inefficiencies in software and data

usage.

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During the development of vehicles and vehicle parts, a huge mass of data is compiled. The problem arises to make the data accessible in a standardized manner, for post-processing tools. The standardization of data modelling and data retrieval was a big challenge for the automobile industry and their suppliers. The challenge was accepted by the ASAM consortium, which has developed during the past 18 years a mature solution for data archiving and retrieval in the domain of product testing. Today, the standard is available and the automobile car industry in Europe requests from its suppliers the implementation of this standard as part of their test bed automation systems.

1. The name of the association is:

"Verein zur Förderung der internationalen Standardisierung von Automatisierungs- und Meßsystemen (ASAM)".

2. The association is registered in the Register of Associations and bears the addition “registered”.

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3. The association's head office is Munich.

4. The association's business year is the calendar year.

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Detailed road maps are an important building block for autonomous driving. They accelerate creating a semantic environment model within the vehicle and serve as a backup solution when sensors are occluded or otherwise impaired. Due to the required detail of maps for autonomous driving and virtual test drives, creating such maps is quite labor-intensive. While some detailed maps for fairly large regions already exist, they are often in different formats and thus cannot be exchanged between companies and research institutions. To address this problem, we present the first publicly available converter from the OpenDRIVE format to lanelets—both representations are among the most popular map formats. We demonstrate the capabilities of the converter by using publicly available maps.

The approach for simulation-based testing of assisted and automated driving (ADAS/AD)

functions does not differ substantially from the one taken for other control functions: The system

under test needs to be separated and embedded in a plausible simulation of the remaining

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system. While most control functions can be interfaced on a signal-based approach,

ADAS/AD functions typically require input from environment sensors – often in form of object

lists. Although their content is widely comparable across different implementations, the structure

of the complex data and the interpretation details differ significantly. This hampers the

interfacing of simulation tools to ADAS/AD functions and the exchange of object lists between

different tools alike, because custom mappings are not only tedious to provide, but also error

prone. ASAM OSI can help to overcome these hurdles, leading to an easier, more reliable and

re-usable interfacing between different tools and with the system under test.

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Standardisation on interfaces and

formats for CCAM validation

Dr. Oihana Otaegui

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Director of ITS and Engineering

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