RC Car Racing Challenge

Introduction

Time

Team (setting 10min + racing 10min)

Hardware

 

ROUND 1

 
  09:50 - 10:10

HYBRID

rccar 1

  10:00 - 10:20

MELON_MUSK

rccar 2

  10:10 - 10:30

CALIFORNIAN_HOUNDS

 rccar 1

  10:20 - 10:40

MAZE_RUNNER

rccar 2

  10:30 - 10:50

GOTOMARS

rccar 1

  10:40 - 11:00

RACECAR

rccar 2

 

ROUND 2

 

  10:50 - 11:10

HYBRID

rccar 2

  11:00 - 11:20

MELON_MUSK

rccar 1

  11:10 - 11:30

CALIFORNIAN_HOUNDS

rccar 2

  11:20 - 11:40

MAZE_RUNNER

rccar 1

  11:30 - 11:50

GOTOMARS

 rccar 2

  11:40 - 12:00

RACECAR

 rccar 1

  12:00 - 12:30

  Score Announcement

 
 
Team Name
Member 1
Member 2
Member 3
1
RACECAR
LEE JONG HOON
LEE YONG HEE
MUN CHAIYONG
2
CALIFORNIAN HOUNDS
BAE BYEONG UK
OH JIN HO
SHIM JOON YOUNG
3
GOTOMARS
KIM JIN MYEONG
LEE SUNGYOUNG
JANG JI HA
4
HYBRID
CHOI JIN YOUNG
SHIN JAE WOO
JI SE MIN
5
MAZE RUNNER
OH SEUNGTAEK
LEE TAEK LIM
KIM HOOYOUNG
6
MELON MUSK
KIM PETER YONGHO
BAE SO YOON
JEONG MUN KYU

This contest will show off the autonomous driving with a RC car. Winners will be chosen based on the number of passed waypoints and race completion time in real race tracks.


Schedule

 

 

Goal

The objective of this contest is to encourage entrants to create a robust and fast robot driving policy to control an RC car with LiDAR. Students have to use behavior cloning (GPR) or implement a deep learning algorithm for driving policy. The map of the environment is not given to RC Car, so the RC car needs to estimate its control input using the LiDAR observations. The goal of the contest is to complete race in the fastest time without collision.

 

 

  

 

 

 

Details

Racing Track

Driving Policy

The driving policy generates an efficient path to race without colliding on track. To generate a proper action that the RC car can follow, dynamics of the RC car (e.g. rotation angle, speed, ...) should be considered while generating actions.

Teams