Driving agents trained in one simulated town often perform poorly in a new town because the road shapes, intersections, and lane layouts can be different. This paper studies how to improve this kind of transfer in the CARLA driving simulator without giving the agent any training data from the test towns. The agent is trained only in Town05 and Town06, then evaluated directly in Town03 and Town04. To focus on road-layout differences, all experiments use the same weather and traffic settings. We propose a training method that encourages the agent to learn features that are useful across towns rather than features tied to one training town. During training, the agent is asked to predict the high-level visual meaning of future camera views and is also discouraged from relying on cues that reveal which source town the data came from. These extra learning signals are used only during training; at test time, the driving policy uses the same observation and control interface as the baseline agent. In controlled comparisons with matched DreamerV3-style world-model driving agents, the proposed method achieves the highest mean held-out success: 36.6\% on Town03 with a 95\% confidence interval of [30.5, 42.7] and 85.6\% on Town04 with a 95\% confidence interval of [84.0, 87.2], computed across five training seeds. Seed-paired tests against the strongest primary baselines show positive success-rate differences in both held-out towns. Additional experiments show that predicting future visual meaning alone or removing town-specific cues alone is not enough to match the combined method. These results suggest that combining future-scene understanding with reduced reliance on source-town-specific features can improve cross-town driving performance in this CARLA setting.

翻译：在单一模拟城镇中训练的驾驶智能体在新城镇中往往表现不佳，这是因为道路形状、交叉口和车道布局可能存在差异。本文研究如何在CARLA驾驶模拟器中提升这种迁移能力，且无需向智能体提供测试城镇的任何训练数据。智能体仅在Town05和Town06上训练，然后直接在Town03和Town04上评估。为聚焦于道路布局差异，所有实验均采用相同的天气和交通设置。我们提出一种训练方法，鼓励智能体学习跨城镇有用的特征，而非局限于单一训练城镇的特征。训练过程中，智能体被要求预测未来摄像头视图的高层视觉语义，同时被阻止依赖可揭示数据来源城镇的线索。这些额外学习信号仅用于训练阶段；在测试时，驾驶策略使用与基线智能体相同的观测与控制接口。在与DreamerV3风格世界模型驾驶智能体的受控对比中，所提方法在两个未见城镇上取得了最高的平均成功率：Town03为36.6%（95%置信区间[30.5, 42.7]），Town04为85.6%（95%置信区间[84.0, 87.2]）（基于五个训练种子的计算）。与最强基线方法的配对种子检验显示，两个未见城镇的成功率差异均为正值。额外实验表明，仅预测未来视觉语义或仅移除城镇特定线索均无法达到联合方法的效果。这些结果表明，将未来场景理解与降低对源城镇特定特征的依赖相结合，能够提升该CARLA设定下的跨城镇驾驶性能。