Transition to autonomous trucks (ATs) is coming, and is expected to create both challenges and opportunities for the driver workforce. This paper presents a novel methodology for identifying viable occupational transitions for truck drivers as transport automation advances. Unlike traditional workforce transition analyses that focus primarily on skill similarity, wages, and employment demand, this methodology incorporates four integrated components: task-level automation analysis, skill similarity assessment, labour market conditions analysis, and empirical validation using historical transition patterns. Applying this methodology to Australian truck drivers shows that while ATs will automate core driving tasks, many non-driving responsibilities will continue requiring a human, suggesting occupational evolution rather than wholesale displacement. A skill similarity analysis identifies 17 occupations with high transferability, while labour market analysis reveals significant trade-offs between wage levels and job availability across potential transition pathways. Key findings indicate that bus and coach driving, along with earthmoving plant operation, emerge as high-priority transition options, offering comparable wages and positive employment growth. Delivery and forklift driving present medium-priority pathways with abundant opportunities but lower wages. A regression analysis of historical transitions confirms that skill similarity, wage differentials, geographic accessibility, and qualification requirements all significantly influence actual transition patterns, with some viable pathways currently underutilised. The research provides policymakers, industry stakeholders, and educational institutions with evidence-based guidance for supporting workforce adaptation to technological change. The proposed methodology is generalisable beyond trucking to other sectors facing automation.

翻译：向自动驾驶卡车（ATs）的转型即将到来，预计将为驾驶员劳动力带来挑战与机遇。本文提出了一种新颖的方法论，用于在运输自动化进程中识别卡车驾驶员可行的职业转型路径。与主要关注技能相似性、工资水平和就业需求的传统劳动力转型分析不同，该方法论整合了四个组成部分：任务级自动化分析、技能相似性评估、劳动力市场条件分析，以及基于历史转型模式的实证验证。将该方法论应用于澳大利亚卡车驾驶员的分析表明，尽管自动驾驶卡车将自动化核心驾驶任务，但许多非驾驶职责仍需人力完成，这暗示了职业演化而非大规模替代。技能相似性分析识别出17种具有高可转移性的职业，而劳动力市场分析揭示了潜在转型路径中工资水平与职位可获得性之间的显著权衡。关键发现指出，公交车与长途客车驾驶以及土方机械操作成为高优先级转型选项，提供相近的工资水平与积极的就业增长。配送与叉车驾驶则呈现中等优先级路径，机会丰富但工资较低。对历史转型的回归分析证实，技能相似性、工资差异、地理可达性及资格要求均显著影响实际转型模式，且部分可行路径目前未被充分利用。本研究为政策制定者、行业利益相关方及教育机构提供了基于证据的指导，以支持劳动力适应技术变革。所提出的方法论可推广至卡车运输以外的其他面临自动化的行业。