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.

翻译：自动驾驶汽车目前因对交通交互中人类行为的不确定性而呈现出低效的驾驶风格。准确可靠的预测模型能够实现更高效的轨迹规划，从而使自动驾驶汽车在此类交互中更具决断力。然而，当前对这类模型的评估通常过于简单化，忽视了预测误差的非对称重要性以及测试所用数据集的异质性。我们探讨了将车辆间的交互重构为间隙接受场景，并在这种结构化环境中评估模型的潜力。为此，我们开发了一个框架，旨在促进任何模型、通过任何指标、在任何场景下的评估。随后，我们将该框架应用于最先进的预测模型，结果发现这些模型在最关键的安全场景中均表现出不可靠性。