Izvorni znanstveni članak
https://doi.org/10.7307/ptt.v38i4.1226
Optimisation of Decision Efficiency for Autonomous Driving at Unsignalised Intersections Based on DRL and GPT
Bojun LIU
; Faculty of Engineering, University of Sydney, Sydney, Australia
Sažetak
The rapid increase in urban vehicle numbers has intensified traffic congestion and safety challenges. Unsignalised intersections pose significant difficulties for autonomous vehicle decision-making. To enhance decision efficiency and safety in such scenarios, this study proposes a decision optimisation method for autonomous driving at unsignalized intersections. The approach first employs a generative pre-trained transformer (GPT) to learn complex interactive behaviour patterns from driving data and acquire prior knowledge. This prior knowledge is then used to initialise the policy network of a deep reinforcement learning (DRL) agent, specifically deep q-network (DQN), which is further optimised through interaction within a simulated environment. This framework aims to combine the powerful sequence modelling capability of GPT with the goal-directed optimisation strength of DRL. Experimental results demonstrate that the proposed method achieves superior performance: The median safe distance reaches 19.58 m (maximum 32.50 m, minimum 8.46 m), the collision rate is as low as 1.07%, and the success rate exceeds 98%. Compared to baseline methods, the proposed approach significantly improves decision-making efficiency and safety for autonomous vehicles at unsignalised intersections, validating its effectiveness.
Ključne riječi
deep reinforcement learning; driving decisions; intelligent driving; generate pre-trained transformation models
Hrčak ID:
346668
URI
Datum izdavanja:
28.4.2026.
Posjeta: 19 *