Recognizing traffic signaling gestures through automotive sensors.

As technology advances with each new day, so do the applications and uses of the different modalities of technology, including transportation, particularly in ADAS vehicles. These systems allow the vehicle to avoid collisions, change lanes, adjust the vehicle’s speed, and more without the need of driver input. However, each sensor type has a weakness, and most advanced driver- assisted system (ADAS) vehicles rely heavily on sensors, such as RGB cameras, radars, and LiDAR sensors. These visual-based sensors may collect very noisy data in cloudy, raining, foggy, or other obscuring phenomena. Radar, on the other hand, does not rely on visual information to produce meaningful output, and instead collects range and velocity information. This research aims to use radar technology for human motion classification using traffic signaling based on motions generally used in the American traffic system, while also fusing data from other visual sensors and validating results using neural networks