November 14, 2023, PALO ALTO, Calif. - Today, Hesai Technology (Nasdaq: HSAI) announced an automotive lidar design win with Great Wall Motors (hereinafter referred to as "GWM"). Multiple passenger vehicle models from GWM will be equipped with Hesai's ultra-high resolution long range lidar AT128, with plans for mass production and delivery starting in 2024.

Lidar is unaffected by changes in ambient light, providing 3D perception capability with high reliability. As a result, it has become a critical sensor for smart vehicles. GWM's urban NOH, based on a perception-focused approach, has been widely implemented as an advanced driver-assistance system. In the future, Hesai AT128 will serve as a key sensor for GWM's series production vehicles, combined with GWM's high computational power and mass-producible ADAS computing platform IDC3.0 to meet intelligent driving requirements in various road scenarios.

GWM is one of the largest automakers in China. In the field of intelligent driving, GWM possesses comprehensive R&D capabilities for both software and hardware. Its independently developed intelligent driving systems have already been deployed in over 20 vehicle models. Additionally, GWM is leading the industry in high computational platforms and intelligent driving software.

Real AT128 Point Cloud

Hesai not only empowers smart vehicles with robust, automotive-grade, and scalable production capabilities, but also integrates lidar manufacturing processes with the R&D phases. This integration facilitates fast product iteration while ensuring high-quality standards. In recent years, Hesai has rapidly expanded its business, working with 14 OEMs and Tier-1 clients, securing lidar design wins for over 50 series production vehicle programs.

The collaboration between Hesai and GWM will continue to advance intelligent driving through perception technology, elevating the experience of smart travel for consumers worldwide.

 

TrendForce 2023 Infrared Sensing Application Market and Branding Strategies
Release: 01 January 2023
Format: PDF
Language: Traditional Chinese / English
Page: 164