上下文感知智能车辆
Context-Aware Intelligent Vehicles
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中文总结 AI 辅助
本综述针对智能车辆多类应用的严格性能要求，提出将上下文情境因素作为核心原则，梳理相关SOTA方法并明确四大技术挑战，以推动相关应用开发。
中文摘要 AI 辅助
智能车辆除自动驾驶外，正日益支持自适应应用，涵盖上下文感知的高级驾驶辅助系统（ADAS）、自动驾驶、座舱内监测及车队管理，所有应用均对精度、延迟、成本和可靠性有严格要求。要满足这些要求颇具挑战，因为车辆在复杂、不确定且快速变化的环境中运行，且依托资源受限的计算平台。本文提出，赋予传感器信号意义并约束决策的上下文情境因素，应被视为下一代车辆系统的核心原则，可作为统一共享状态应用于整个软件栈的学习、风险评估及闭环控制。我们系统综述了涵盖（i）环境理解、（ii）规划与控制、（iii）安全与安保、（iv）联网车辆的上下文感知方法的最新技术（SOTA）。基于对上下文感知设计的趋势分析，我们确定了构建未来智能车辆通用上下文引擎的四大关键技术挑战：多模态上下文融合、时序上下文建模、稀有事件处理及协作上下文共享。希望本综述能推动健壮高效的上下文感知车辆应用的开发。
英文摘要
Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-cabin monitoring and fleet management, all under tight requirements on accuracy, latency, cost, and reliability. Meeting these requirements is challenging because vehicles operate in complex, uncertain, and rapidly changing environments while running on resource-constrained computing platforms. This paper argues that context-situational factors that give meaning to sensor signals and constrain decisions-should be treated as a first-class principle for next-generation vehicle systems, and operationalized as a unified, shared state for learning, risk assessment, and closed-loop control across the software stack. We systematically review state-of-the- art (SOTA) context-aware methods spanning (i) environment understanding, (ii) planning and control, (iii) safety and security, and (iv) connected vehicles. Based on a trend analysis of context-aware design, we identify four key technical challenges in building a general contextual engine for future intelligent vehicles: multi-modal context fusion, temporal context modeling, handling rare events, and collaborative context sharing. We hope this survey will motivate the development of robust and efficient context-aware vehicle applications.
发表机构
- University of Michigan（密歇根大学）
- General Motors（通用汽车公司）
机构由 AI 辅助整理，请以论文原文为准。
