Autonomous Mobility

On the way from level 1 to 5

Considerable advances have been made in research and development in the field of autonomous driving in recent years. This is evident from the development of algorithms for environment detection sensors, data analysis using machine learning and the creation of complex driving functions. In projects involving automated driving (SAE levels 3 and 4) and autonomous driving (SAE level 5), Bertrandt’s expertise also covers areas such as localisation, connectivity and cloud applications.

Over the last few years Bertrandt has gained in-depth experience of developing automated and autonomous driving strategies. Major progress has been made in processing sensor data from camera, ultrasound, laser, lidar and radar systems (environment detection), in developing software and functions for the lateral and longitudinal control of vehicles and in trajectory planning for route calculation.

The challenge of sensor data fusion

Autonomous driving systems need comprehensive virtual images of the environment. These show the vehicle’s location and indicate any actions that are possible or necessary. Among other things, this requires a highly accurate localisation function, which can be made even more precise by identifying landmarks using cameras and radar.

The result is that very large volumes of data have to be processed in real time at speeds of more than 20 Gbit/s. Over the course of a journey lasting a few hours, tens of terabytes of data will accumulate. This leads to the need for huge data storage facilities and high-performance computing systems of the kind that we have implemented in the form of an analytics platform in the Microsoft Azure cloud. We have also been working to overcome the challenges of data labelling.

Software and functions

Trajectory planning
Lateral and longitudinal control
Environment detection
Nvidia PX2
Automotive Ethernet
Localisation
Navigation Data Standard (NDS)
Development processes based on ISO 26262 (functional safety)
Model-based development
AI-based development

Developing algorithms for sensors

Bertrandt has continuously improved the process of developing algorithms for environment detection sensors over the course of a number of internal and customer projects in the automotive industry. One key feature of these projects is the acquisition of expertise in artificial intelligence, machine learning and neural networks.

Another important aspect is the necessary in-depth knowledge of the camera, ultrasound, laser and lidar technologies used in automated vehicle functions. Bertrandt has been developing these technologies over several years for driver assistance systems such as adaptive cruise control and traffic jam, emergency braking and parking assistants.

On this basis Bertrandt has produced automated longitudinal control systems for production models which evaluate the speed limit, the shape of bends in the road and the distance from the vehicle ahead. The lateral control systems developed by Bertrandt include lane departure warning systems that are suitable for production use.

Technologies

Domain architecture
Automotive Ethernet
Longitudinal and lateral control
Systems engineering

Connectivity for autonomous driving

Cars with automated and autonomous driving functions share data with back-end and cloud systems and with nearby vehicles. Bertrandt has completed a range of different internal and customer projects in the field of connectivity.

Car connectivity
Bi-directional data sharing with the Bertrandt Automotive Cloud
Automotive Ethernet, SOME/IP, service-oriented architecture (SOA)
Over-the-air services, dynamic map updates
Vehicle security concepts, secure data transmission and data protection mechanisms

More about Connectivity

Analysing vehicle data

The number of sensors used for autonomous driving is continuing to increase, as is their resolution. As a result, the ability to manage large volumes of data for automation, digital test processes and integration is becoming increasingly important. Bertrandt also has expertise in developing and validating measurement systems, using reference sensors and running sensor evaluations.

In order to move closer towards the self-driving car, connectivity with other vehicles and with cloud and server structures is being enhanced. This allows up-to-date information about the weather and the traffic situation, for example, to be evaluated. Bertrandt is developing systems that enable the front-end, the back-end and the cloud platform to communicate with one another. These include both apps for mobile devices and web interfaces.

Developing cloud applications

Microsoft Azure platform, Microsoft Data Science Virtual Machines, Microsoft Data Lake Store
Data analytics
Connectivity and back-end systems
Device connectivity
TLS/PKI encryption

Artificial intelligence in vehicles

One important finding to emerge from our internal and customer projects is that a technology company such as Bertrandt, with expertise in all areas of vehicle development and design, has considerable advantages over specialist IT businesses. More than just a knowledge of AI and IT is needed to develop algorithms for image recognition and sensor data analysis and to predict vehicle behaviour.

At Bertrandt, data scientists work in cross-disciplinary teams with specialists in vehicle design and dynamics in order to evaluate how a car will behave in a corner under lateral and longitudinal acceleration. Neural networks help to categorise objects on the road and adjust the vehicle control systems to accommodate scenarios such as steering, braking or taking evasive action. It is only possible to develop plausible and successful algorithms on the basis of a comprehensive understanding of the overall design and behaviour of the vehicle.

Internal and customer projects

Innovation project to develop a level 4/level 5 car
Park and Charge
Lidar projects
Driverless transport systems

Testing and validating autonomous driving functions

As the level of automation in cars increases, so the methods used to test and validate new driving functions become more important. Bertrandt has been developing HiL, SiL and MiL tests on the basis of its extensive automotive expertise for 40 years. This allows us to combine the progress made towards developing autonomous vehicles with the testing of algorithms and the validation of new driving functions. We have developed user studies in driving simulators, simulation processes for virtual road testing, physical road trials and endurance and long-term tests. Bertrandt’s competence lies in the ability to test individual components, integrated systems and the interaction of all the systems needed for automated driving.

Validating autonomous driving functions

Supplying test equipment (including a mobile solution)
Test facilities for predictive pedestrian protection and emergency braking, for example
Complying with the testing requirements of the 2018 EuroNCAP protocol
Evaluating emergency lane keeping functions

Safeguarding by means of environment sensors

Test benches for the safeguarding of radar and LiDAR sensors
Sensor performance evaluation
Bundling of expertise, testing know-how and a detailed evaluation
Test kilometers saved due to the simulation of driving and environmental scenarios in the laboratory
Acceleration of sensor development times

More about sensor technologies