Autonomous Vehicles (AVs) are transforming the future of transportation through advances in intelligent perception, decision-making, and control systems. However, their success is tied to one core capability, reliable object detection in complex and multimodal environments. While recent breakthroughs in Computer Vision (CV) and Artificial Intelligence (AI) have driven remarkable progress, the field still faces a critical challenge as knowledge remains fragmented across multimodal perception, contextual reasoning, and cooperative intelligence. This survey bridges that gap by delivering a forward-looking analysis of object detection in AVs, emphasizing emerging paradigms such as Vision-Language Models (VLMs), Large Language Models (LLMs), and Generative AI rather than re-examining outdated techniques. We begin by systematically reviewing the fundamental spectrum of AV sensors (camera, ultrasonic, LiDAR, and Radar) and their fusion strategies, highlighting not only their capabilities and limitations in dynamic driving environments but also their potential to integrate with recent advances in LLM/VLM-driven perception frameworks. Next, we introduce a structured categorization of AV datasets that moves beyond simple collections, positioning ego-vehicle, infrastructure-based, and cooperative datasets (e.g., V2V, V2I, V2X, I2I), followed by a cross-analysis of data structures and characteristics. Ultimately, we analyze cutting-edge detection methodologies, ranging from 2D and 3D pipelines to hybrid sensor fusion, with particular attention to emerging transformer-driven approaches powered by Vision Transformers (ViTs), Large and Small Language Models (SLMs), and VLMs. By synthesizing these perspectives, our survey delivers a clear roadmap of current capabilities, open challenges, and future opportunities.

翻译：自动驾驶车辆（AVs）正通过智能感知、决策与控制系统的进步，重塑未来交通格局。然而，其成功与否取决于一项核心能力：在复杂多模态环境中实现可靠的目标检测。尽管计算机视觉（CV）与人工智能（AI）领域近期取得突破性进展，该领域仍面临关键挑战——知识在多模态感知、上下文推理与协同智能方面仍呈碎片化。本综述通过前瞻性分析自动驾驶车辆中的目标检测技术来弥合这一鸿沟，重点探讨视觉语言模型（VLMs）、大语言模型（LLMs）与生成式AI等新兴范式，而非重新审视过时技术。我们首先系统回顾自动驾驶车辆传感器（摄像头、超声波、激光雷达与雷达）的基本体系及其融合策略，不仅强调其在动态驾驶环境中的能力与局限，更凸显其与近期LLM/VLM驱动感知框架进展相结合的潜力。接着，我们提出超越简单数据集的自动驾驶数据集结构化分类体系，涵盖自车数据集、基于基础设施的数据集以及协同数据集（如V2V、V2I、V2X、I2I），并对数据结构与特征进行交叉分析。最后，我们剖析前沿检测方法——从2D与3D检测流程到混合传感器融合技术，特别关注由视觉Transformer（ViTs）、大/小语言模型（SLMs）及VLMs驱动的新兴Transformer方法。通过综合这些视角，本综述清晰描绘了当前技术能力、开放挑战与未来机遇的发展路线图。