Demonstrators
NewControl partners dveloped demonstrators to show the potential of the project to facilitate perception, cognition, control validation and user acceptance of next generation highly automated vehicles.
SC1 Robust and accurate adaptive perception systems
SC1
FMCW-LIDAR with 3D fully non-mechanical scanning
MEMS LIDAR with 3D non macro-mechanical scanning
Fiber optical gyroscope
Inertial MEMS sensors (gyro and accelerometer)
Camera system
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SC2
Next generation embedded software platform for autonomous driving
Low-power high-performance HW architecture
Embedded platform for raw image procession into point clouds in combination with a true solid-state LIDAR
Computational HW to show the fail-operational capability of the HW platform
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SC3
Fail-operational vehicle decision making and control
Vision-based driver/passenger monitoring using neural networks
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SC4
Propulsion platform
Battery platform
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SC6
Simulator: Extraurban obstacle avoidance
Simulator: Suburban takeover
Passenger Vehicle: Extraurban obstacle avoidance
Passenger Vehicle: Urban parking
Passenger Vehicle: Urban intersection
Passenger Vehicle: Predictive lifetime monitoring
Heavy Duty Vehicle: Construction-site parking
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SC7
Virtual twin validation toolkit
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SC8
End user acceptance of automated driving
Inertial MEMS sensors (gyro and accelerometer)
MEMS inertial sensors have a possibility to improve the robustness of vehicle localization based on perception sensors. LiDAR, radar and cameras are influenced by snow, water mist or dirt.
Other perception principles, inertial sensing does not require any visual information outside the vehicle and can be used to complement or improve the accuracy of vehicle localization under harsh conditions. Z-axis gyroscopes (Yaw-rate sensors) can be used for keeping the car in the lane for a short period but accurate accelerometers are needed to compensate the gyro axis errors. A very innovative new possible application of inertial sensing is finding of true north direction by measuring the earth rotation with extremely sensitive MEMS gyroscopes. It can determine heading of a vehicle in conditions that are not possible using other methods.
The goal of the new acceleration sensor is the application in autonomous driving (e.g., inertial navigation) and for that, a 3-axis version is needed.
SC2 Robust energy-efficient processing architectures for embedded fusion and control
NG Embedded Software Platform for Autonomous Driving
The main purpose of this demonstrator - Fail-operational SoA-based SW Platform Architecture for Fusion & Control.
Demo KPIs (Technology Brick for Vehicle MW/SW for NG of highly automated vehicles SAE 4 to SAE 5):
•Service code does not need to be changed when a different service is updated, even when new/changed interfaces are introduced.
•Service code does not need to be changed when the location of a service changed.
•Abstraction of operating system, communication and programming languages is provided: Services can be deployed to different operating systems and developed in different programming languages.
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The exploitation of result:
Enabling SoA to the NG Safety SW Platform denotes a paradigm-shifting technology brick for highly automated driving SAE level 4&5. The NG Safety SW Platform will offer abstraction from the communication layer, seamless integration of application SW modules as well as hypervisors and OSs, and virtualization.
SC3 Virtual platforms for holistic decision making and control
Simulation Environment
The primary goal of the technology enabler SC3 was to design and implement adaptive and predictive motion planning and control algorithms (which consider vehicle limitations, environment situation, and perception limitations) to achieve fail-operational holistic decision making and control enabling super-human driving performance in terms of safety, efficiency, comfort, and performance.
Main Results:
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Demonstration of fail-operational vehicle decision making & control system achieving super-human performance
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Simulation environments for verifiable and certifiable approaches.
Partners delivered virtual environments for all specified use cases
within two defined demonstrators.
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Demonstrator 3.1:
• 5 simulation environments for 6 use cases
• 22 SW building blocks to be developed and verified
• 10 partners contributing to the 5 simulation architectures.
Demonstrator 3.2:
• 1 simulation environment for 1 use case
• 5 SW building blocks to be developed and verified
• 3 partners contributing to the simulation architecture.
SC4 Virtual platforms for stable and efficient propulsion
Electric Drive Platform
Main aims
•Reduce NVH by solving a trade-off between efficiency and noise/vibrations.
•Increase the efficiency and lifetime of powertrain components by predictive and reactive adaptive control strategies.
•Develop the Smart Diagnostic Sensor (SDS) with data fusion from different vibration sensors and data analysis on the edge and test it on a testbench.
Final picture
The constructed powertrain was rigorously tested in the BUT laboratory. A new SiC inverter was built by BUT and connected with the motor designed by AVL. The control algorithms capable to suppress the unwanted vibrations were developed, implemented and compared with the classical vector control. The developed sensor was mounted to the motor, connected via CAN bus to NI cDAQ hardware and compared to Polytec Scanning Vibrometer measurement system.
KPIS
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NVH force reduction through control optimization by 3dB – achieved vibration reduction is between 0 to 30 percent which fulfils committed 3 dB reduction.
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Extend powertrain operation to reach full lifetime (10000 hours which is equiv. to 300000 km) of road vehicles depending on application through e-Machine control improvement – The conjunction of efficient design, flexible control and monitoring of component degradation (as is discussed in SC2: Lifetime monitoring and predictive maintenance) is strongly facilitating this goal.
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Reduce failure rates of automated vehicles through diagnostics provided by smart components in the system, and thus reduce downtime – achieved by the SDS and real time monitoring of vibrations
SC6 Virtual platforms for stable and efficient propulsion
Fail-operational vehicle decision making and control
Unikie demonstrates two different fail recovery scenarios which are defined in the D2.7 but with passenger car and in parking garage. In other words, how car can execute the different parking maneuvers independently with onboard equipment if car loses the connection to external control unit.
UNIKIE has been developed infra based autonomous driving setup, based on calibrated stationary sensors, edge computing unit and localisation, path planning, driving and collision avoidance algorithms. Functional Safety and Fail Recovery are built-in into system design. Vehicle’s mission and high-level path planning will happen in external Behaviour Planning tool called Control Hub.
Vehicle is controlled by infra based system, which utilizes lidars, cameras and mission control computing unit. Communication with car is done over 5G network. In demo scenario vehicle is executing mission to drive certain route which is set from the mission control computer. There appears connection failure between mission control unit and the vehicle. Vehicle detects the connection error and switch to autonomous driving mode controlled by onboard equipment and successfully finish the mission. When connection is restored, vehicle continues with control of infra based control system.
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