Interview - Baselabs' Eric Richter on sensor fusion : Page 4 of 4

June 07, 2019 //By Christoph Hammerschmidt
Interview - Baselabs' Eric Richter on sensor fusion
The automation of driving requires an immense amount of software in the vehicles, also and above all in the area of sensor data fusion. The software company Baselabs has gained a strong position in this area. With Baselabs co-founder and director customer relations Eric Richter, eenews Europe talked about the software requirements and the role artificial intelligence will play in cars.

eeNews Europe: How will Sensor Data Fusion develop? We are dealing with ever more powerful computer platforms that deliver ever larger amounts of data. Will this area reach its limits at some point? Must the peripheral assemblies become more intelligent so that they can reduce their data volumes?

Richter: We at Baselabs believe that the centralization of data fusion will continue. There are already some manufacturers like Audi that are going down this path - with central, high-performance computing platforms to also make a central data fusion. Yes, the amount of data will continue to increase, but so will the data quality that is processed there. In my opinion, the trend will go towards raw data fusion, especially if you address higher-value driving functions. Especially from autonomy level 3 onwards we see a clear trend towards raw data fusion in central fusion control units.

eeNews Europe: Would artificial intelligence be a possible approach to take over tasks in the field of data fusion?

Richter: This is quite conceivable and currently the subject of a lot of research. At present, however, methods based on artificial intelligence can be applied almost exclusively to the data of individual sensors such as cameras. For data fusion, tracking and plausibility checking of data from several sensors such as camera, radar and lidar, classical data fusion approaches as downstream components are still very effective and thus the ideal complement to AI-based methods. Another big question in the use of artificial intelligence is how to secure the system, how to arrive at a security architecture for such systems.

eeNews Europe: Is this to be understood as meaning that artificial intelligence is not compatible with the functional safety standard ISO 26262, which always tends to enforce a very deterministic behavior?

Richter: Exactly. There are a lot of discussions about ISO 26262 in this area - it is not perfectly designed for the use of artificial intelligence. Irrespective of ISO 26262, the question will soon arise as to how much artificial intelligence can be trusted in safety-critical systems and how long a parallel backup path can be maintained. It is quite clear that in some scenarios AI-based procedures will provide better performance and thus, for example, enable more comfortable driving. This is undisputed, but it is questionable whether they can always achieve this. Here we say, like many others in the engineering community, that we don't know this 100% yet. For safety reasons, therefore, we must continue to run classic data fusion processes in parallel. This makes it possible to establish a safe state if the two procedures are not in agreement. For this, the classic procedures are still needed - at least that is how we and large parts of the community regard it.

Related articles:

Vector, Baselabs join forces to develop ADAS tools

A Developer’s View of ISO 26262

How Automotive Displays can meet Functional Safety

Can we trust our cars?

Global alliance develops standards for automated-driving tests

 


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