Related SdV standards

This SdV inter::op proof point is supporting the following standards:

COVESA VSS specification
COVESA VSS language mappings: Python

SdV inter::op use case

Travel sickness (Kinetosis) can be expected in 80% of all children aged eight during a car journey. Although the driver himself is rarely affected by Kinetosis, distraction by passengers poses a significant safety risk. Kinetosis symptoms range from malaise, difficulty concentrating and cold sweats to nausea and vomiting. Although each person has an individual sensitivity, 64% of those affected show mild symptoms and 46% show severe symptoms, resulting in significantly worsened performance. The development of Kinetosis is based on a sensory conflict between visual (sense of sight), proprioceptive (body sensitivity) and vestibular perception (sense of balance). A typical example is reading a map while driving. Here, you see a static image (map), while movements are perceived via the muscle groups and the vestibular organ in the inner ear. The Anti-Kinetosis-App counteracts the occurrence of travel sickness by determining the driving behaviour through continuous sensor analysis and its evaluation. If the risk of developing Kinetosis is too high, it is signaled to the driver (HMI) and a recommendation is given to prevent the worst by adjusting the driving style or speed for critical route sections.

Relevant products

The following products were used in this SdV inter::op proof point:

Company	Product	Role in SdV tool chain
IPG Automotive GmbH	CarMaker	Full vehicle simulation tool that provides relevant information for a Kinetosis App to evaluate the Kinetosis Level.
Ansys	Ansys optiSLang	Used to find the optimum between relaxed and sportive driver regarding time used at scenario staying lower than a defined Kinetosis level
Ansys	Ansys SCADE	Implementation of control to switch between drive modes during run-time

SdV inter::op architecture

The following describes the inter::op architecture used for the Kinetosis app. The simulation results including the vehicle signals, as well as the corresponding video data are generated by CarMaker (IPG Automotive) and exported as files. optiSLang (ANSYS) is used for driver parameter optimization. A custom VSS / CarMaker adapter is making the simulation results available to the digital.auto Playground. In the Playground, the Kinetosis app is implemented in Python, using the standized Python mappings for VSS. This will ensure that the Kinetosis app can later on easily be ported to an SdV runtime running on board a real vehicle, accessing real sensor data via the same VSS interfaces.

Replicability

On a scale including "Use case-specific | Easily replicable | Fully productized", this proof point has been classified as "Easily replicable".

Reason: Architecture can be re-used for other use cases easily

Benefits for SdV tool chain

Being able to use advanced vehicle simulation tools like CarMaker (IPG Automotive) and ANSYS enables the SdV developer to test his SdV applications in near real-world conditions, without the overhead of having to built up a physical simulation environment. Because a standardized vehicle API is used (COVESA VSS), the application can later on be easily ported from the simulation environment to a real vehicle, using real sensor data as input. This approach is significantly reducing upfront investments, and helps developing features with high customer value. New applications can be tried out in a lightweight prototyping environment, which can be used to get early feedback from end-users – allowing developers to revise and fine-tune their applications early in the development cycle.