AUTOMOTIVE
Steer toward full vehicle autonomy with confidence:
Moving from the road to the lab Silviu Tuca, radar-based autonomous vehicle product line manager for Keysight Technologies discusses the reality of achieving fully autonomous vehicles
C
reating safe and robust automated driving systems for future vehicles is a complex task. There are immediate challenges that automakers must overcome to realise the future of autonomous mobility. Autonomous vehicles have hundreds of sensors, that all need to work in concert within the car and with other smart vehicles in their surrounding environment. The software algorithms enabling autonomous driving features will ultimately need to synthesise all the information collected from these sensors to ensure the vehicle responds appropriately. The vision of fully autonomous vehicles is looming and along with improving the overall efficiency of transportation systems, driver and passenger safety is the most compelling advantage of self-driving vehicles.
Level up vehicle autonomy Advanced driver assistance systems in
production vehicles have reached levels two and three, which in most traffic situations, require the driver to control the vehicle. Many original equipment manufacturers (OEM) and industry experts believe pushing further toward levels four and five autonomy - where five represents vehicles not requiring any human interaction - will make our roadways safer.
To achieve the next level in vehicle autonomy, many advancements are required. There will be massive investments in sensor technologies, such as radar, lidar, and camera, which will continue to improve environmental scanning. As each sensor type has its own advantages and disadvantages, they need to complement each other to ensure the object detection process has the required built-in redundancy.
Huge investments in computationally powerful software algorithms are also
18 DECEMBER/JANUARY 2023 | ELECTRONICS TODAY
necessary to combine and carry the large amount of high-resolution sensor data including vehicle-to-everything (V2X) communication inputs. Machine learning (ML) is the established method for training self- improving algorithms and artificial intelligence (AI). Those algorithms are then making decisions to ensure safety in complex traffic situations. Training these algorithms with the most realistic stimuli available, in a repeatable and controlled fashion in the lab, is crucial for their accuracy and their safe deployment.
The gap between roadway and software simulation testing
Today, a large amount of testing time is spent focused on sensors and their control modules (ECUs) by simulating environments in software or software-in-the-loop (SIL) testing. Road testing of the completely integrated system within a prototype or road-legal vehicle allows
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44
