Measurement Performance of Sensor Systems towards Autonomous Vehicles

The tutorial will focus on sensor and measurement systems for new generations of vehicles with driver-assisted/autonomous capability. This is the main trend that is revolutionizing vehicles and mobility of people and goods and is also making smart our cities. The economic and social impacts of this application field are huge. Worldwide every year 90 million vehicles are sold, but 1.25 million people are killed due to lack of safety. In the US 3.1 billions of gallons of fuel are wasted due to traffic congestion. Assisted driving and autonomous driving aim at increasing safety, at improving fuel efficiency and our lifestyle by avoiding traffic congestion, at ensuring mobility for elderly and disabled people (inclusivity). The interest in this research subject is demonstrated by the huge investments of companies like Google, Intel, Tesla, Uber, Ford, GM, to name just a few, and by technology alliances, e.g. between BMW and Intel, planning autonomous cars for 2021. A convergence between automotive and ICT/Electronics industry is foreseen in the near future. An example of this convergence is the 5G Automotive Association http://www.5gaa.org/, which includes all main cars’ manufacturers, telecom service providers, electronic industries, measurement system providers (Keysight, Rohde&Schwarz).

The key enabling technologies for this scenario are the sensing and measurement systems, needed for the accurate vehicle positioning and navigation, for vehicle context-awareness, obstacle detection, and collision avoidance, for driver-assistance (enhanced vision, driver’s attention and fatigue detection).

The lecture will be divided into multiple sections.

First, in the Introduction, innovation and market trends in the field of sensor and measurement technologies applied to vehicles and smart mobility systems will be discussed, focusing on next generation of driver-assisted/autonomous vehicles.

Then, new Radar and Lidar systems, appearing on-board vehicles beside an array of imaging cameras, will be discussed for measurement of obstacle positions, distance, and relative speed. A trade-off has to be found between the power and size of active sensing systems like Radar and Lidar and their maximum measurement range. Moreover, in continuous wave Radars, the limited frequency sweep range and the limited number of TX/RX channels lead to limits for the resolution in distance, direction of arrival, and speed measurements. Examples of X-band mobility surveillance Radar and mm-wave automotive Radar will be provided.

On the other hand, MOEMS (micro opto electro mechanical systems)-based scanned systems, used to reduce size and cost of Lidars are causing distortions that are worsening the accuracy of light-based measurements. Distortions due to fish-eye lenses, used to enlarge the field-of-view, are decreasing measurement performance of imaging sensors. Techniques to mitigate such artifacts will be discussed.

Practical examples of traffic sign recognition systems, road signs recognition, image mosaicking for all-around view will be discussed. In addition, Lidar and imaging cameras suffer from decreased measurement performance in case of harsh operating conditions (e.g. bad weather or light conditions).

New biometric sensing and measurement systems will be also reviewed, such as Radar-based contactless heart/breath-rate measurement, smart steering-wheel for skin temperature/galvanic-response measurements or heart-rate detection, with the final aim of detecting the driver’s attention or health status.

Concerning on-board sensors for positioning and navigation, recent advances in MEMS accelerometers and gyroscope will be discussed. A careful analysis will be carried out about the measurement errors they cause on position and navigation, due to their bias and random walk output noise.

Finally, the lecture will analyze the trend in computing platforms, where parallel architectures and machine learning/AI (artificial intelligence) techniques, will be exploited to manage in real-time many and heterogeneous sources of measurements and to make autonomous decisions.

Suggestions for future directions of interest for the I&M Society, and references to recent publications on IMS journals and conferences, in the field of automated and connected vehicles, will be provided as a conclusion.