EMDRIVE
Mannheim EMDRIVE is a BMBF-funded project for the conception and realtime(RT)-compatible extension of central computing platforms and embedded compute networks for future highly automated vehicles. The project network consists of several industry partners from the automotive industry accompanied by partners from academia.
The overall goal of the partners in the course of the project is the development of a hierarchical, scalable platform concept for centralized high performance automotive RT-compute boardnets (Sensor2Edge) and the transfer of results to automotive industry.
This can be broken down into the following subgoals within the project:
- Embedded RT Compute Performance
- Power Consumption
- Dynamic, Distributed Computing
- RT-Monitoring and Diagnosis
Our Contribution: RT Monitoring and Diagnosis
Modern vehicles already host 100+ Electronic Control Units (ECUs) running more than 100 million lines of code—and software complexity will only grow with autonomous-driving features. Because many sporadic, software-induced faults escape lab testing, vehicles need an in-field mechanism to observe, diagnose, and adapt throughout their lifetime.
Our solution, the Diagnosis Unit (DU), delivers exactly that. Built as a modular HW/SW co-design that can be retro-fitted at a gateway’s mirror port, the DU provides cross-layer, real-time monitoring while remaining virtually invisible to normal in-vehicle operation.
How It Works
- Ethernet-Traffic Snooping
- The DU taps mirrored traffic on the zonal gateway and applies threshold- and ML-based analytics to detect anomalies such as timing irregularities, message-injection patterns, or traffic-burst deviations.
- Microcontroller Trace Analysis
- When a network anomaly is flagged, the DU remotely configures the target ECU’s Multi-Core Debug Solution (MCDS) via TAS, records a short execution trace, and analyzes CPU load, control-flow deviations, and timing violations.
- Dynamic Anomaly Tree (DAT)
- A runtime-reconfigurable tree links communication symptoms to likely processing-level root causes. The backend can update this tree on-the-fly across an entire fleet, turning every car into a collaborative diagnostics sensor.
- Edge-to-Cloud Workflow
- Heavy data (raw traces) is processed locally on a Zynq UltraScale+ ZCU102 platform (PL for packet parsing, PS for analysis). Only compact summaries—typically a few kB—are uploaded for fleet-wide orchestration and expert review, minimizing bandwidth overhead.
Why It Matters
- Reconfigurable & Future-Proof – Cloud-driven updates let the DU adapt to new fault patterns without workshop visits.
- Minimal Intrusion – Operates on mirrored Ethernet traffic; no extra latency or bandwidth impact on the IVN.
- Comprehensive Coverage – Correlates system-level network symptoms with component-level execution traces, enabling root-cause insight instead of mere symptom reporting.
With its flexible architecture and fleet-coordinated intelligence, the Diagnosis Unit pushes automotive runtime monitoring beyond static OBD routines—empowering engineers to uncover and fix elusive, software-driven faults long after vehicles leave the factory floor.
Current work
- Simulation of Zonal In-Vehicle Network Architectures
Ongoing development and evaluation of Ethernet-based zonal IVNs using OMNeT++, modeling Time-Sensitive Networking (TSN) behavior and ECU communication flows across mirrored gateway ports. - AI-Based Ethernet Anomaly Detection
Implementation of anomaly detection mechanisms using deep learning models such as LSTM autoencoders and Transformer architectures in Python (TensorFlow/Keras). These models aim to identify timing irregularities, unexpected traffic bursts, and ID sequence anomalies in live Ethernet traffic. - AUTOSAR Integration on Infineon AURIX (TC397)
Development of diagnostic and control logic on Infineon AURIX TC397 microcontrollers, including AUTOSAR-compliant applications responsible for real-time vehicle sub-functionality (e.g., lane detection, control logic, actuation). - Real-Time Microcontroller Trace Collection via MCDS
Leveraging the Multi-Core Debug Solution (MCDS) integrated in the AURIX TC397 to collect fine-grained execution traces. These traces are triggered and retrieved via the Tool Access Socket (TAS) server interface, and subsequently analyzed to detect processing anomalies such as excessive instruction duration, memory misaccesses, or execution flow deviations. - Trace Analysis and Cloud-Coordinated Diagnosis
Development of a modular Trace Analyzer hosted on the Zynq UltraScale+ MPSoC, capable of identifying anomalous runtime behavior through timing and control-flow analysis. Diagnostic results are compiled into compact summaries and sent to a centralized cloud backend, which maintains a Dynamic Anomaly Tree (DAT) and orchestrates fleet-wide diagnosis strategies through over-the-air reconfiguration.
Open Work
If you are interested in any of the current work items that are mentioned above and there is currently no open position, please do not hesitate to contact Zafer Attal.
Completed Work
- Non-Intrusive Monitoring of Core Utilization on a Multicore Automotive Control Unit
(Master Thesis, 2023) - Simulation of Zonal-Architecture Intra-Vehicular Network with TSN Functionality
(Teaching Assistant, 2022–2023) - Implementation of a Real-Time Diagnosis Unit Prototype on ZCU102
Designed and implemented a hardware/software co-design on a Zynq UltraScale+ MPSoC (ZCU102), splitting diagnostic tasks between the Programmable Logic (PL) for Ethernet packet parsing and the Processing System (PS) for trace analysis and backend communication. - Integration of Anomaly Detection in Ethernet Traffic
Configured the DU to detect specific Ethernet anomalies such as timing deviations, burst inconsistencies, and traffic pattern changes, using empirical thresholds and real traffic profiles. - Aurix ECU Trace Triggering and Retrieval via TAS
Connected the DU to Infineon Aurix TC397 boards using TAS to remotely configure, trigger, and retrieve execution traces via the Multi-Core Debug Solution (MCDS). - Demonstration Setup and Validation in a Simulated Function Chain
Implemented a testbed with three Aurix boards simulating a Lane Keeping Assistant (LKA). Successfully validated end-to-end detection of communication anomalies, triggering of trace capture, and analysis of processing deviations.