台師大陳仲崴

This paper presents a fully onboard embedded autonomous race track system targeting the FIRA Challenge – Autonomous Cars (Pro) Race Track task. The system is built on a Carson T410R 1:10 scale RC car equipped with a Raspberry Pi 4B single-board computer, a monocular camera, and a BMI160 IMU. A Fast-SCNN semantic segmentation network fine-tuned on 53,000 race-track frames achieves mIoU = 0.732 and maintains approximately 56–58 FPS under full ROS 2 node load on the Raspberry Pi 4B. A segmentation-to-path pipeline converts dashed-line masks into navigation target points via connected-component centroid extraction and linear polynomial fitting. An IMU gyroscope feedforward term pre-corrects camera servo angle to compensate for the ~40 ms visual latency inherent to the embedded pipeline. A raceoriented control logic integrates nonlinear camera-to-wheel steering, rate-limited wheel commands, line-loss stopping, obstacle-triggered steering offsets, curvature-based speed limiting, lateral-G throttle reduction, and IMU-failure safe mode. Kinematic simulation on an elliptical track yields mean lateral error 0.67 cm (max 2.66 cm). Practice-track testing shows approximately 90% corner success with autonomous line-recovery capability. The system operates entirely onboard with no wireless control links, satisfying FIRA Rule 2.8 and qualifying for the onboard-processing autonomy bonus (Ka=1.0).

台師大陳仲崴, LIU, CHIA-HENG Verified by AVIS CertificateFIRA World Summit 2026