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Explore cutting-edge data capture devices like the RIDDER hardware and learn how they integrate with autonomous vehicle technologies. This course covers various hardware components, GPS units, and sensor integration techniques for data collection and analysis in real time. Discover datasets such as COCO and Cityscapes for machine learning applications. Dive into important aspects like Adaptive Cruise Control and Lane Keep Assist in automotive engineering.
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Introduction aux SystèmesEmbarqués (notions d’apprentissage) Sujets Spéciaux en Informatique II PIF6004
Aspects importants • Capture de données • Jeux de données pour l’apprentissage machine • Référence: MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation, MIT
Capture des données (ex: informations capturées) • Adaptive Cruise Control (ACC) • Pilot Assist (Volvo) • Super Cruice (Cadillac) • Forward Alert Warning / City Safety (Volvo) • Automatic Emergency Braking • Lane Departure Warning (LDW) • Lane Keep Assist (LKA) • Blind Spot Monitor
Capture des données (Dispositif matériel:RIDDER) • 1GHz ARM Cortex-A7 processor, 1GB of RAM • Expandable GPIO ports for IMU/GPS/CAN • Native onboard SATA • Professionally manufactured daughter board for sensor integration • ARM processor features onboard CAN controller for vehicle telemetry data collection • Maxim Integrated DS3231 real-time clock for accurate timekeeping/time-stamping +/-2 ppm accuracy
Capture des données (Dispositif matériel:RIDDER) • Texas Instruments SN65HVD230 CAN transceiver • 9 degrees-of freedom inertial measurement unit (STMicro L3GD20H(gyro), LSM303D(accelerometer/compass)) • GlobalTop MTK3339 GPS unit, 6 channel, DGPS capability accurate within 5 meters • • Huawei E397Bu-501 4G LTE USB module • USB 3.0 4-port hub, powered • 1TB/2TB solid state hard drive
Capture des données (Jeux de données utiles) • COCO • KITTI • Cityscapes • CamVid