论文标题
在差距接受方案中预测人类行为的模型的基准
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
论文作者
论文摘要
自动驾驶汽车目前遭受了由于人类行为在交通互动中的不确定性而造成的时空驾驶方式。准确，可靠的预测模型实现更有效的轨迹计划可以使自动驾驶汽车在这种相互作用中更加自信。但是，对此类模型的评估通常是过于简单的，忽略了预测错误的不对称重要性以及用于测试的数据集的异质性。我们研究了在这种结构化环境中，将车辆之间的相互作用重新铸造相互作用的潜力。为此，我们开发了一个框架，旨在通过任何指标和任何情况下通过任何指标对任何模型进行评估。然后，我们将此框架应用于最先进的预测模型，这些模型在最安全的情况下都表明自己是不可靠的。
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework aiming to facilitate the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.
