Finally, we expose these lane lines to the rest of the vehicle’s autonomous system, futher being consumed by the path planning subsystem to allow the car to stay centered in its lane. After this step is complete, our system performs post-processing to generate second-order polynomial representations for each lane line. This model will be run on an FPGA in the car, which has been specially customized for this task, in hopes of giving us a performance boost over running it solely on the CPU. We trained a deep neural network to generate segmentations from a camera image, labelling each pixel in the image as belonging to a lane line or not. Our team, WatonomoVision, completely redesigned the lane detection stack by using deep learning to tackle the problem. At Waterloo, the WATonomous student self-driving car team consists of over 100 members, each with an integral part in helping the team succeed in the Challenge. While autonomous vehicles are emerging in the automotive market, SAE International and General Motors have partnered to introduce the AutoDrive Challenge, where teams of students from eight universities across North America compete in building a level 4 autonomous vehicle. These systems, particularly in vehicles designed for operation without a driver, require computationally intense calculations and decision that need to be made instantaneously. #INTERFACE HAS LOST CONNECTION WITH PASCO CAPSTONE DRIVER#With a majority of new vehicles sold today featuring driver assistance technologies such as collision avoidance and lane departure warning systems, the idea of a self-driving car is not all too far off on the horizon.
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