Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
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DOI：
10.48550/arxiv.2302.00064
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Rhys Howard;L. Kunze
中科院分区：
文献类型：
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作者：
Rhys Howard;L. Kunze
自主机器人需要推理其环境中动态代理的行为。描述这些关系的模型的创建通常是通过应用因果发现技术来完成的。然而，就目前情况而言，观察因果发现技术很难充分应对自治代理领域在线使用过程中常见的因果稀疏性和非平稳性等条件。同时，由于领域限制，介入技术并不总是可行。为了更好地探索观测技术面临的问题并促进对这些主题的进一步讨论，我们对自动驾驶领域的 10 种当代观测时间因果发现方法进行了基准测试。通过根据从现实世界数据集提取的因果场景以及综合生成的因果场景来评估这些方法，我们强调了需要进行改进的地方，以促进因果发现技术在上述用例中的应用。最后，我们讨论了未来工作的潜在方向，这些方向可以帮助更好地解决当前最先进技术所遇到的困难。
Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. Finally, we discuss potential directions for future work that could help better tackle the difficulties currently experienced by state of the art techniques.