用反事实分析自监督训练模型，实时判断自动驾驶中物体重要性。
Self-Supervised Relevance Modelling in Autonomous Driving via Counterfactual Analysis
- 通过反事实分析构建自监督相关性模型，识别影响决策的关键物体。
- 在高密度场景下实现毫秒级延迟的实时相关性估计。
- 生成热力图揭示驾驶策略，适用于感知与规划系统优化。
自动驾驶依赖计算密集的感知流水线持续检测和跟踪周围环境中的物体。尽管部分物体对制定安全有效的驾驶动作至关重要，但其他物体可能无关紧要，不会影响车辆决策。聚焦于相关物体可更高效利用计算资源，降低处理延迟，并减少感知噪声的下游传播。本文提出一种基于反事实分析的新型自监督方法，用于构建相关性模型——一种量化物体对自动驾驶车辆重要性的智能工具。为验证该方法潜力，我们在选定城市场景中生成的合成因果数据集上训练相关性模型。结果表明，该模型能够以毫秒级延迟准确估计物体的相关性，实现在高密度场景下的实时估计。此外，相关性模型还可生成相关性热力图，为理解自动驾驶车辆的驾驶策略提供洞见，并可用于主动指导感知与规划任务。相关模型与因果数据集已公开发布。
原文摘要 · Abstract (English)
Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. While some objects are key to plan safe and effective maneuvers, others may not be relevant and have no impact on the autonomous vehicle's driving decisions. Focusing on relevant objects allows a more efficient usage of available computational resources, reduces processing latencies, and limits the downstream propagation of perception noise. In this work, we propose a novel self-supervised approach based on counterfactual analysis to develop a relevance model - an AI-based tool that quantifies the relevance of objects for an autonomous vehicle. To demonstrate the potential of the proposed approach, we train a relevance model on a synthetic causal dataset generated in a selected urban scenario. Results show that the relevance model is able to accurately estimate the objects' relevance with millisecond-level latency, enabling real-time relevance estimation also in high-density scenarios. We also show that the relevance model can be used to build relevance heatmaps that offer valuable insights into the autonomous vehicle's driving policy and can be used to proactively inform perception and planning tasks. We openly release both the relevance model and the causal dataset.
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