Accurately modeling the behavior of traffic participants is essential for safely and efficiently navigating an autonomous vehicle through heavy traffic. We propose a method, based on the intelligent driver model, that allows us to accurately model individual driver behaviors from only a small number of frames using easily observable features. On average, this method makes prediction errors that have less than 1 meter difference from an oracle with full-information when analyzed over a 10-second horizon of highway driving. We then validate the efficiency of our method through extensive analysis against a competitive data-driven method such as Reinforcement Learning that may be of independent interest.

翻译：准确建模交通参与者的行为对于自动驾驶汽车在密集车流中安全高效地导航至关重要。我们提出了一种基于智能驾驶员模型的方法，该方法仅使用少量帧和易于观测的特征，就能准确建模个体驾驶员的驾驶行为。在高速公路上行驶的10秒预测范围内，该方法产生的预测误差与拥有全部信息的理想模型相比，平均差值小于1米。随后，我们通过与具有竞争力的数据驱动方法（如强化学习）进行广泛对比分析，验证了我们方法的效率，这一对比分析本身可能具有独立的研究价值。