End-to-end approaches are one of the most promising solutions for autonomous vehicles (AVs) decision-making. However, the deployment of these technologies is usually constrained by the high computational burden. To alleviate this problem, we proposed a lightweight transformer-based end-to-end model with risk awareness ability for AV decision-making. Specifically, a lightweight network with depth-wise separable convolution and transformer modules was firstly proposed for image semantic extraction from time sequences of trajectory data. Then, we assessed driving risk by a probabilistic model with position uncertainty. This model was integrated into deep reinforcement learning (DRL) to find strategies with minimum expected risk. Finally, the proposed method was evaluated in three lane change scenarios to validate its superiority.

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process. World models have emerged as a transformative approach, enabling autonomous driving systems to synthesize and interpret vast amounts of sensor data, thereby predicting potential future scenarios and compensating for information gaps. This paper provides an initial review of the current state and prospective advancements of world models in autonomous driving, spanning their theoretical underpinnings, practical applications, and ongoing research efforts aimed at overcoming existing limitations. Highlighting the significant role of world models in advancing autonomous driving technologies, this survey aspires to serve as a foundational reference for the research community, facilitating swift access to and comprehension of this burgeoning field, and inspiring continued innovation and exploration.