Driving into the Future: A Comprehensive Survey of Autonomous Vehicle Technologies<b></b>

Engineering notes

Beyond exploring essential architectural components, it evaluates the modern state of research by benchmarking the three dominant autonomous vehicle software architectures: modular pipelines, pure End-to-End (E2E), and hybrid systems across seven quantitative dimensions: planning quality, safety certification, latency, data efficiency, debuggability, Operational Design Domain (ODD) adaptation, and robustness. Comparative analysis of industry-standard benchmarks, including nuScenes and CARLA, reveals that while E2E and hybrid approaches achieve superior planning scores (88–91%) and lower collision rates (1.1–2.0%) than modular pipelines (84–86% planning; 3.95% collisions), pure E2E models lack a viable regulatory path for 2026 L4 deployment due to prohibitive validation mandates.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous vehicles (AVs) represent a foundational cornerstone of future smart city transportation systems, offering the potential to eliminate human driving errors, reduce traffic fatalities by at least 40%, and optimize energy consumption. While AV technology is advancing rapidly toward highly automated driving (HAV) and driver-out Level 4 (L4) deployment, commercial realization remains hindered by significant technological uncertainties, soaring development costs, and strict safety-critical validation requirements. This provides a comprehensive, holistic survey of autonomous vehicle technology, bridging gaps in the existing literature by tracing its historical evolution to modern platforms equipped with LiDAR, radar, cameras, and (V2X) communication. Beyond exploring essential architectural components, it evaluates the modern state of research by benchmarking the three dominant autonomous vehicle software architectures: modular pipelines, pure End-to-End (E2E), and hybrid systems across seven quantitative dimensions: planning quality, safety certification, latency, data efficiency, debuggability, Operational Design Domain (ODD) adaptation, and robustness. Comparative analysis of industry-standard benchmarks, including nuScenes and CARLA, reveals that while E2E and hybrid approaches achieve superior planning scores (88–91%) and lower collision rates (1.1–2.0%) than modular pipelines (84–86% planning; 3.95% collisions), pure E2E models lack a viable regulatory path for 2026 L4 deployment due to prohibitive validation mandates. Conversely, hybrid modular-E2E algorithms recover approximately 98% of E2E planning performance, drastically reduce debugging times to 2–6 hours, and retain compliance with ISO 26262:2018 ASIL-D safety standards. This work maps out the market landscape and identifies hybrid architectures as the current Pareto-optimal solution, balancing operational capability, safety assurance, and immediate regulatory feasibility for L3/L4 automation

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