Abstract:
An autonomous vehicle’s behavior prediction function predicts nearby vehicle states based on current and past environmental observations. This raises their risk awareness. In simple driving scenarios with short prediction horizons, conventional behavior prediction solutions work. Due to their superior performance in complex environments, deep learning-based approaches have become popular recently. This article reviews deep learning-based vehicle behavior prediction methods, inspired by their rising popularity. We first discuss the generic problem of vehicle behavior prediction and its challenges, then classify and review the latest deep learning-based solutions based on input representation, output type, and prediction method. The article also evaluates several popular solutions, identifies research gaps, and suggests new research directions.
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