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Distinguished Lecture Reports


IMS at the Heart of the

Autonomous, Connected and Electrified Vehicle Revolution


Sergio Saponara, IEEE IMS DL, University of Pisa


The race towards Autonomous, Connected and Electrified (ACE) vehicles will revolutionize the mobility of people and goods, with a tremendous social and economical impact.  Every year more than 1 million people are killed worldwide due to mobility injuries, a number that will be remarkably reduced by the adoption of higly automated vehicles (6 levels according to the SAE classification, from L0-no automation to L5-full autonomous cars). The quality of life of people will be further increased by the reduction of traffic jams, ensured by a better planning of traffic flows, and by the increased mobility (and hence independence) ensured to elderly and disabled people. Traffic jam reduction, combined with the electrification of the powertrain, and a better exploitation of renewable energy sources for electricity production (e.g. photovoltaic panels), will ensure less emissions of CO2 and of other pollutants in our cities. The digital connectivity of vehicles (V2X-vehicle to everything) will ensure their full integration with our "digital lifestyle". Also the mobility of goods (i.e. logistics) will experience great benefits thanks to ACE vehicles. The high economic value of such revolution is witnessed by the huge investments of traditional automotive industry, and by the fact that big ICT players (e.g. Google, Intel, Nvidia to name just a few) are entering the mobility application field.

Instrumentation and measurement (IM) systems are key enabling technologies for the ACE revolution.

First of all, ACE vehicles need an increased awareness of the surrounding environment and of relevant obstacles. To this aim, new high performance measuring systems are needed where data coming from camera, radar, ultrasonic and lidar sensors are fused to increase detection capability of targets, and to know with cm-level accuracy their position, as well as their relative speed and motion direction.

Vehicle positioning and trajectory planning need also accurate maps and geolocalization capability, that can be achieved by proper combining low-cost IMU (Inertial Measurement Unit) and GNSS (Gobal Navigation Satellite System) services. V2X, either based on WLAN (IEEE802.11-p/-ad) or cellular (4G-LTE/5G) technologies, will need accurate radio resource monitoring and allocation  to maximize goodput and to reduce latency, thus increasing  connection reliability.

It is both a matter of R&D of new technologies (e.g. integrated radars operating at mmWaves, with electronic scanned antenna arrays; integrated photonics to produce Lidars affordable for large volume car production); MEMS accelerometers and gyroscopes with low bias instability and low angular random walk) and of new signal processing and data understanding algorithms. The latter include not only deterministic and statistical digital techniques, but also machine learning and deep neural networks.

Similar requirements come from the electrification trend, where for example high performance lithium-based energy storage claim for a mix of physical (reaction temperature, voltage, current, pressure, deformation of the cells) and virtual (e.g. extended kalman or AI estimators) sensors to detect state-of-health, state-of-charge, and state of safety, residual life and allowing for predictive diagnostic capability.   

The above challenges are further exacerbated since ACE vehicles operate in harsh environments (for temperature, humidity, vibration, Electromagnetic Interference, over-current/voltage), but with high levels in terms of functional safety and security. Hence, new verification and testing procedures, as well as new standards (e.g. ISO26262), have to be developed.

The tremendous increase of the computational requirements of new sensing and measurement systems for ACE vehicles can be satisfied by the availability of energy-efficient High Performance Computing (HPC) processors. To this aim, the European Processor Initiative is mixing multi-core 64b general purpose tiles, with Scalable Vector extension, High Bandwidth Memories and accelerators tiles for cryptography, AI, big data analytics, power monitoring/management, variable-precision and reconfigurable (embedded FPGA) computing. Mixing the HPC number crunchers with a safe & secure controller (MCU) in a multi component automotive platform allows for reaching the above goals.

Summarizing, as discussed in the IEEE IMS DL of Prof. Saponara (the last ones invited in Nancy and Bordeaux by the IEEE I&M France Chapter)  sensing and measurement technologies are at the heart of the ACE vehicle revolution, with high social and economic impacts. The new generation of ACE vehicles represents also an interesting R&D platform for the academic and industrial community of the IEEE I&M society. 



[1]  Saponara, S., Neri, B., “Radar sensor signal acquisition and multi-dimensional FFT processing for surveillance applications in transport systems “, (2017) IEEE Transactions on Instrumentation and Measurement, 66 (4), pp. 604-615

[2] Saponara, S., Greco, M.S., Gini, F., "Radar-on-Chip/in-Package in Autonomous Driving Vehicles and Intelligent Transport Systems: Opportunities and Challenges", (2019) IEEE Signal Processing Magazine, 36 (5), pp. 71-84.

[3] Bello, L.L., Mariani, R., Mubeen, S., Saponara, S., "Recent Advances and Trends in On-Board Embedded and Networked Automotive Systems", (2019) IEEE Transactions on Industrial Informatics, 15 (2), pp. 1038-1051

[4] Pieri, F., Zambelli, C., Nannini, A., Olivo, P., Saponara, S., "Is Consumer Electronics Redesigning Our Cars?: Challenges of Integrated Technologies for Sensing, Computing, and Storage", (2018) IEEE Consumer Electronics Magazine, 7 (5), pp. 8-17"

[5] Saponara, S., Ciarpi, G., "Electrical, Electromagnetic, and Thermal Measurements of 2-D and 3-D Integrated DC/DC Converters". (2018) IEEE Transactions on Instrumentation and Measurement, 67 (5), pp. 1078-1090

[6] Saponara, S., et al., “Predictive Diagnosis of High-Power Transformer Faults by Networking Vibration Measuring Nodes With Integrated Signal Processing”, (2016) IEEE Transactions on Instrumentation and Measurement, 65 (8) pp. 1749-1760