AUTOMOTIVE ELECTRONICS
Steer Toward Full Vehicle Autonomy with Confidence
Silviu Tuca, the radar-based autonomous vehicle product line manager for Keysight Technologies, explains the challenges to overcome when moving from the road to the lab…
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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 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
32 MARCH 2023 | ELECTRONICS TODAY
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 OEMs to validate the final product before bringing it to market. Recreating a virtual world in the lab, with accurate rendering of the scenes, plus real radar sensors and signals, will bridge the gap between simulation and road testing.
The challenge today is the emulation of full radar scenes, especially when the scenes are complex and have many variables. The goal is to thoroughly test in the lab all driving scenarios, even the corner cases, before bringing the vehicle to the test track or open roadways.
Software simulation is used in the early development cycle. Simulation is possible of underlying sensors, vehicle dynamics, and weather conditions. Is that all it takes? Is it good enough to confirm what has been tested in plain simulation can now be taken to the real world? The software is ultimately an abstract
view, and it has imperfections.
Relying only on real-world road testing is also unrealistic because it would take millions of meters for vehicles to become safely reliable to navigate in urban and rural roadways 100% of the time. To truly test the AV/ADAS functionality, it is necessary to control all relevant parameters.
To close the gap between real world testing and simulation, real and physical sensors are needed in the test setup. This complexity must be added to the test to predict how AVs will behave on the road.
Under any circumstance, the vision is for technology to fully replace the human behind the wheel to enable reliable, accurate, and safe decisions on the road. Software simulation cannot fully test the real sensor response and testing on the track is not repeatable. Today, specifically when emulating radar targets, there are several technology gaps.
Limited number of targets and field of view
A common approach ties each simulated target to a delay line. Even if additional targets are added, only one radar echo is processed at a time. Also, if an antenna array is created, it isn’t possible to simultaneously emulate targets at the extreme ends of the radar module’s field of view. In addition, each movement of
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