Intrusion detection is crucial for safeguarding intelligent vehicle systems, aiming to identify abnormal network traffic and operational anomalies. Traditional methods primarily focus on spatial features of attacks, often neglecting temporal dynamics essential for detecting complex, evolving threats. Additionally, the effectiveness of existing techniques is limited by the scope and quality of available datasets, reducing their ability to detect novel, unseen attacks. To address these challenges, this paper introduces a Transformer-based Transfer Learning Intrusion Detection System (TIDS), designed to capture and analyze spatiotemporal sequence features from vehicle data. TIDS generates high-dimensional feature representations of intricate intrusion patterns, improving the detection of known attack types through instance-based transfer learning, enhancing domain adaptability. Moreover, we propose a novel self-supervised box classification method that enhances the system's capability to detect previously unknown attacks, thereby increasing the overall robustness of the intrusion detection process. Comparative experiments demonstrate that TIDS outperforms traditional methods in detection speed and accuracy across various intrusion scenarios, effectively responding to emerging threats in intelligent vehicle networks.