The automated (SAE level 3 and 4) and autonomous (SAE level 5) driving functions in the vehicles of tomorrow require connectivity and bi-directional data sharing via online services. However, there are still a number of obstacles to be overcome in order to achieve the necessary degree of connectivity. Bertrandt has acquired extensive expertise in this area on behalf of car manufacturers and automotive industry suppliers and is making use of new development methods.
Over recent years we have worked on a number of internal and customer projects involving new developments in connectivity in areas that include:
Vehicle security concepts, secure data transmission and data protection mechanisms
Bertrandt takes an integrative development approach to autonomous vehicles, connectivity and electric mobility. We develop intelligent autonomous systems that can react and make decisions independently on the basis of information from the vehicle’s environment. For example, in a project to develop an autonomous parking system (using online data, cameras and lidar), we sent sensor data to the cloud and processed it using algorithms and artificial intelligence. The output of this process was a set of instructions which were sent back to the vehicle. This enabled it to steer, brake and move independently.
Examples of projects
In projects to develop connected assistance systems, we are resolving problems such as how to continue using automated driving functions without an online connection (bridging data, interpolation mechanisms etc.). This includes concepts relating to data handling, data compression and data storage in the vehicle.
Alongside existing bus systems, such as the CAN bus, car connectivity will in future require new domain architectures and will increasingly consist of IP- and Ethernet-based systems (Automotive Ethernet). Our approach to implementing new services in this area is based on the concept of the service-oriented architecture (SOA).
Starting points
A highly accurate localisation function is needed for autonomous driving. We use high definition maps to allow vehicles to navigate to within the nearest centimetre. In future connected cars will download the latest maps automatically. We develop navigation algorithms in accordance with the Navigation Data Standard (NDS). (Bertrandt is a member of the NDS Association.)
We have created precise localisation concepts for a number of internal and customer projects with features that include:
A wide range of sensor data is processed for use in automated driving functions in order to record road conditions and identify and classify objects (environment detection). The data is analysed, the vehicle’s behaviour is calculated for steering purposes and the route is planned. Large volumes of data have to be transmitted and processed in order to provide connected assistance functions of this kind. In addition, details of weather and traffic conditions are recorded by nearby vehicles. This can make the car aware of an accident around a blind bend and enable it to identify a new route in the event of congestion (swarm intelligence concept).
Bertrandt uses a connectivity device for communicating with other vehicles and transmitting data to back-end systems and the cloud.
The Automotive Analytics and Development Platform developed by Bertrandt is a solution for analysing sensor data and creating algorithms using artificial intelligence. The Bertrandt Automotive Cloud is our own system for the connected car, with data fusion, data analysis, machine learning and algorithm development. We have made use of our extensive experience of machine data logging (Bertrandt Industry Cloud) and solutions for outdoor driverless transport systems, together with our collaborations with several manufacturers.
Key features
Safety and security considerations become even more important in autonomous vehicles because connectivity increases the risk of hacker attacks. Unauthorised access could result in a third party taking control of the vehicle, the incorrect use of autonomous functions and the theft of sensitive data. The goal is to prevent unauthorised parties from manipulating the vehicle’s information. In addition, we are developing methods for anonymising and pseudonymising data.
Key features
