New Delhi: Functions for highly automated driving must be intensively validated by means of simulations. In the AVEAS research project, Porsche Engineering is working on automated detection of critical traffic situations from sensor data using AI and storing the situations in a database. The route models and traffic situations generated in this way are also varied in order to generate more test cases for virtual validation. A vehicle overtakes and pulls in again leaving too little distance in front of the car behind—at such moments, accidents are often only narrowly avoided. Engineers specifically increase criticality, for example by reducing the distance between vehicles. “We are building a complete catalogue of critical scenarios that enable us to validate driver assistance systems and functions for highly automated driving,” explain Joachim Schaper, Head of AI and Big Data at Porsche Engineering, and Tille Karoline Rupp, Responsible for Simulation at Porsche Engineering.
Simulatable Scenarios AVEAS aims to eliminate a major hurdle in the path of autonomous driving: lack of data. The aim of the project is to evaluate test drives automatically and to prepare the critical traffic situations as simulated scenarios. Porsche Engineering is contributing a number of key components to this. For example, a JUPITER test vehicle (Joint User Personalized Integrated Testing and Engineering Resource) is being provided for the test drives. It is equipped with cameras, radar, and lidar sensors and sends the data it measures to the cloud. Porsche Engineering also handles the evaluation: Algorithms automatically record the course of the road, the position of other road users, and the road users’ behavior. The machine learning methods used are constantly being refined.
The recorded traffic events are stored in standardized file formats such as ASAM OpenDRIVE (logical description of the road network) or ASAM OpenLABEL (objects and the dynamics thereof). AVEAS can therefore also provide input for other projects, such as route modeling.
The selection algorithm also highlights those kinds of traffic situations so that they can be used to safeguard driving functions. Extension of the test space The virtual test drives take place in the internally developed simulation environment known as PEVATeC SimFramework (Porsche Engineering Virtual ADAS Testing Center Simulation Framework). The real journey can be reconstructed (simulated) and then be played through after specific modifications have been made, all within the digital world.
Porsche Engineering is constructing a digital twin of the JUPITER test vehicle. “The ‘Digital JUPITER’ contains the same interfaces and sensors as the real vehicle,” explains David Hermann, a doctoral candidate and specialist project engineer in the field of simulation at Porsche Engineering. “All functions can be tested on a one- to-one basis.” Porsche Engineering will use the Digital JUPITER to evaluate and optimize an Adaptive Cruise Control function and a parking function (Reverse Assist) within the framework of AVEAS.
AVEAS will be running until the end of 2024, by which time a scalable pipeline for the evaluation of driving scenarios should be in place—as well as a catalog with many hundreds of thousands of critical scenarios. Both could greatly accelerate development work in the future.
“As part of the AVEAS project, the JUPITER test vehicles that are used feed measurement data into the route modeling process. They use their lidar sensors to scan the surroundings and transfer the resulting point clouds into the cloud. Since road markings reflect differently from asphalt, they can be easily identified in the lidar data. Special algorithms calculate a continuous overall line from the individual markers (this process even works if individual markers are missing).
