- Hyundai and IonQ, a Maryland-based developer, are working on ways to interpret sensor data from autonomous vehicle sensors like Lidar using quantum computing.
- Quantum computers are used to perform object detection tasks on three-dimensional data from autonomous vehicle sensors.
- This technology has the potential to significantly speed up the processing of real-world data collected by vehicle sensors in cars with advanced levels of autonomy.
A number of standalone developers, with the notable exception of You’re hereembraced the rapidly changing Lidar (light detection and ranging) technology as part of SAE Level 2 Driver Assistance Systems and higher levels.
The principle behind lidar is relatively simple: a pulsed laser emitter scans its surrounding area, emitting millions of pulses per second to paint a three-dimensional image that the driver assistance system or higher-level autonomous system interprets, detecting objects up to a few hundreds of meters. On-board hardware and software then decide what to do with that data when making automatic driving decisions.
Half a decade ago, these Lidar sensors looked like spinning aluminum cans mounted on the roofs of vehicles, but now they’ve gone solid state and look like little sensor modules on the leading edges of vehicles. roofs of vehicles, just in front of the windscreen.
Lidar sensors and technology have made great strides over the past decade, to the point that each Level 3 system and higher currently on sale or about to hit the market offer Lidar. Still, there is room for the technology to grow.
hyundai and IonQ, a Maryland-based quantum computing developer, recently revealed the next steps of their partnership, applying IonQ’s quantum computers to image processing that should perform object detection tasks on 3D data. from autonomous vehicles.
Specifically, the companies are looking to use IonQ’s quantum computers to simulate the electrochemical reactions of different metal catalysts. In such a case, traffic sign images are encoded in a quantum state for object classification and detection, which greatly speeds up the system’s detection and classification of objects, cars, people and buildings. along the road.
“Quantum machine learning techniques studied at IonQ have shown the potential to learn faster, be more efficient at recognizing edge cases, generalize better, learn from lower resolution or noisy data, and to capture complex correlations with a much lower number of parameters,” the company said. “These profound technical advantages can ultimately lead to faster, safer and more accurate decisions without user intervention.”
hyundai also works with IonQ on chemical reactions and lithium compounds used in EV batteries, studying new chemical reactions of metal catalysts, analyzing them with quantum computers.
“Autonomous vehicles are still in their infancy, but the quantum-derived algorithms we’re testing today have the potential to shape the marketability, efficiency and safety of these systems,” said Jungsang Kim, co-founder and CTO of IonQ.
Currently, autonomous developers are primarily focused on miniaturizing solid-state lidar systems while making them cheaper to manufacture. But it’s refreshing to see companies rethinking how lidar sensors perceive the world around them in the context of Level 3 and Level 4 systemsas both types of systems are almost ready to go to market.