Advancing Highway Safety: Embedded-edge AI for Real-time Applications

In the rapidly evolving landscape of intelligent transportation, the pressing need for real-time Artificial intelligence-based trajectory prediction and anomaly detection in highway scenarios is paramount. Ensuring the safety of highway workers, optimizing traffic flow, and enhancing surveillance mechanisms necessitate advancements tailored for embedded-edge platforms. This dissertation responds to these imperatives by developing a lightweight deep learning model that transitions from traditional LSTMs to leverage the efficiency of Agile Temporal Convolutional Networks, achieving streamlined computational requirements without sacrificing accuracy. An extensive vehicle trajectory dataset is presented, capturing a diverse range of driving scenes and road configurations from 1.6 million frames. To further the field, an innovative vehicle trajectory prediction model is introduced, employing attention-based mechanisms and outperforming existing benchmarks. The research culminates in an integrated AI pipeline optimized for real-time anomaly detection on highways. This system, synergized with a pioneering anomaly-specific dataset, sets new benchmarks in highway safety and surveillance, showcasing the potential of AI-driven solutions in addressing contemporary transportation challenges.