Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown promise in other domains but is lacking in the trajectory forecasting of multiple traffic agents in large-scale autonomous driving datasets. This work investigates the state-of-the-art trajectory forecasting model Trajectron++ which we enhance by incorporating a smoothing term in its attention module. This attention mechanism mimics human attention inspired by cognitive science research indicating limits to attention switching. We evaluate the performance of the resulting Smooth-Trajectron++ model and compare it to the original model on various benchmarks, revealing the potential of incorporating insights from human cognition into trajectory prediction models.

翻译：理解交通参与者的行为对于预测其未来轨迹至关重要，这有助于为自动驾驶车辆开发安全可靠的规划系统。将认知过程与机器学习模型相结合在其他领域已展现出潜力，但在大规模自动驾驶数据集中多交通体轨迹预测方面仍较为欠缺。本研究探究了最先进的轨迹预测模型Trajectron++，我们通过在其注意力模块中引入平滑项对其进行了增强。该注意力机制模拟了人类注意力模式，其灵感来自认知科学中关于注意力切换存在限制的研究。我们评估了由此产生的Smooth-Trajectron++模型性能，并在多项基准测试中将其与原始模型进行对比，揭示了将人类认知见解融入轨迹预测模型的潜力。