统一感知让自动驾驶更鲁棒，整合检测跟踪预测三任务
To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software
- 提出早、晚、全三种统一感知范式，系统分类现有方法
- 整合多任务提升上下文理解与运行效率，减少误差累积
- 适合研究自动驾驶感知融合的学者与工程团队参考
自动驾驶感知通常依赖模块化流水线，将任务分解为检测、跟踪和预测。尽管可解释性强，但存在误差累积和跨任务协同不足的问题。统一感知作为一种新范式，通过共享架构整合多个子任务，有望提升鲁棒性、上下文推理能力与效率，同时保持可解释输出。本文综述统一感知研究进展，提出涵盖任务集成、跟踪形式与表征流的系统性分类框架，定义早、晚、全三种范式，系统梳理现有方法的架构设计、训练策略、使用数据集及开源情况，并指明未来研究方向。本工作建立首个全面理解与推进统一感知的框架，整合分散研究，引导未来向更鲁棒、通用、可解释的感知演进。
原文摘要 · Abstract (English)
Autonomous vehicle perception typically relies on modular pipelines that decompose the task into detection, tracking, and prediction. While interpretable, these pipelines suffer from error accumulation and limited inter-task synergy. Unified perception has emerged as a promising paradigm that integrates these sub-tasks within a shared architecture, potentially improving robustness, contextual reasoning, and efficiency while retaining interpretable outputs. In this survey, we provide a comprehensive overview of unified perception, introducing a holistic and systemic taxonomy that categorizes methods along task integration, tracking formulation, and representation flow. We define three paradigms -Early, Late, and Full Unified Perception- and systematically review existing methods, their architectures, training strategies, datasets used, and open-source availability, while highlighting future research directions. This work establishes the first comprehensive framework for understanding and advancing unified perception, consolidates fragmented efforts, and guides future research toward more robust, generalizable, and interpretable perception.
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