Deep Learning for Self-Driving Vehicles

Abstract

Self-driving vehicles (SDV) and advanced safety features offering the greatest challenges and opportunities for Artificial Intelligence. The understanding of human intention is a very difficult task. As a result, predicting other drivers' future behaviour is critical for perceiving their past motion, analysing their interactions with other agents, and processing the data available from the scene. Automated driving systems (ADSs) promise to make driving safer, more comfortable, and more efficient. The Deep Structured Self-Driving Network (DSDNet) is proposed in this work that uses a single neural network to conduct object identification, motion prediction, and motion planning. The deep structured energy-based model, on the other hand, is improved. DSDNet also takes advantage of the expected forthcoming predicted actors to prepare a safe manoeuvre. Experiments The results reveal that it considerably increases detection, prediction, and planning performance.