Multi-Task in Autonomous Driving through RDNet18-CA with LiSHTL-S Loss Function
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Abstract
Most current autonomous driving research focuses on single-task or dual-task methods. We propose to combine road tracking, obstacle avoidance, traffic sign recognition, and traffic light recognition in a single multi-task framework. Additionally, we validate it using a scale model car to confirm its viability in a semi-physical environment. We propose a novel framework, RDNet18-CA, designed to reduce the training requirements associated with enormous full-scene datasets. These massive full-scene datasets are utilized in the autonomous driving systems of companies such as Google and Tesla. Thus, our framework performs well with small training datasets and can function in unseen scenarios to a certain degree. Additionally, we present an innovative loss function, LiSHTL-S, that exhibits adaptivity. This allows the LiSHTL-S loss function to be dynamically modified based on the properties of the train data and the state of the model throughout the training phase, eliminating the requirement for intense manual parameter tuning. Lastly, we present a new traffic light design concept called the traffic board to enhance its resistance to lighting noise, making it more adaptable for autonomous driving. With these innovations in mind, our method outperforms existing methods in multiple areas.
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