SHARK
Autonomous Driving Competition
In collaboration with the Seongnam City Government in South Korea, MORAI is hosting an autonomous driving algorithm competition using a digital twin map of Seongnam City Hall.
Competition Overview
The 10 finalist teams performed various driving missions using cameras, LiDARs, and radars — including dynamic obstacle avoidance, maintaining safe following distance, and static obstacle avoidance in GPS shadow zones.
Organizers
Seongnam City Government, MORAI
Eligible Participants
Undergraduate and graduate students from universities across South Korea
Why MORAI Training and Competitions
01
Autonomous Driving & Simulation Education :
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MORAI provides official training materials and curriculum to help students learn core concepts of autonomous driving and how to use simulation tools effectively.
02
All-in-One Competition Package :
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Includes dedicated simulation software, live broadcasting systems, and an automated scoring system — everything you need to run a seamless competition.
03
Realistic Digital Twin Environments :
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MORAI delivers custom simulation environments based on digital twins of real-world locations like Seongnam, Sangam, and university campuses — optimized for competition and training.
A digital twin of Yeungnam University Campus
Real World
Digital Twin
More Use Cases
SHARK Autonomous Driving Competition
Digital Twin for Automotive Research
MORAI, in partnership with Team NAVER, the Seoul Metropolitan Government, and the Korea Automobile Testing & Research Institute, successfully hosted the Seoul Virtual Autonomous Driving Competition at Afreeca Colosseum in Sangam, the heart of e-sports.
MORAI has partnered with Seongnam City to support startups, research institutes, and universities in advancing future mobility technologies, including autonomous vehicles, drones, and robotics. Through this collaboration, MORAI provides access to the SHARK (Seongnam Hightech Autonomous Road frameworK) platform and offers specialized technical training.
Scenario Creation for
ADAS & AD
MORAI provides a variety of simulation scenarios designed to detect edge cases, along with digital twin maps for testing them. This enables users to identify potential hazards that vehicles may encounter and understand system limitations that could arise in various situations.
