Ficosa and LeddarTech have signed a development and commercialization agreement for the development of a smart automatic parking assistant.
Ficosa will integrate LeddarVision™ software into their parking ADAS. LeddarVision is high-performance sensor fusion and perception software which provides extremely accurate 3D models of the vehicle’s surroundings, developed using raw data inputs from sensor systems. This raw data fusion and perception software enables the detection of even the smallest obstacles on the road, with fewer false alarms and greater accuracy than with traditional object-based perception solutions. This agreement will enable the full potential of this software to be combined with Ficosa’s leadership in cameras and vision systems in the automotive sector.
Ficosa already offer a range of products to cover all vehicle segments, including an independent rear-view camera; independent intelligent rear-view camera; surround view system, and autoparking system—and they produce more than eight million rear-view cameras a year.
With this announcement, Ficosa continue to strengthen their intelligent in-vehicle parking assistance system with a solution that allows a more detailed, more precise perception of the surroundings. Based on this information, the vehicle will be able to better ‘understand’ the context in which it is moving, and better detect and respond to unexpected objects such as a pedestrian crossing its path.
The agreement between Ficosa and LeddarTech is an important milestone that will enable car manufacturers to offer an improved experience using advanced driving assistance systems. This alliance also represents further progress towards the consolidation of the autonomous vehicle. In the autonomous mobility ecosystem, the collection and analysis of reliable and detailed environmental data is an essential step towards ensuring the highest standards of safety and comfort for users.
The use of lidars in automated parking functions can greatly improve the perception of immediate vehicle’s environment. Such perception is traditionally done by low-cost ultrasonic sensors, which suffer of a lack of spatial resolution—which limits the performance of automation in complex situations such as curb detection.