Safe, socially compliant, and efficient navigation of low-speed autonomous
vehicles (AVs) in pedestrian-rich environments necessitates considering
pedestrians' future positions and interactions with the vehicle and others.
Despite the inevitable uncertainties associated with pedestrians' predicted
trajectories due to their unobserved states (e.g., intent), existing deep
reinforcement learning (DRL) algorithms for crowd navigation often neglect
these uncertainties when using predicted trajectories to guide policy learning.
This omission limits the usability of predictions when diverging from ground
truth. This work introduces an integrated prediction and planning approach that
incorporates the uncertainties of predicted pedestrian states in the training
of a model-free DRL algorithm. A novel reward function encourages the AV to
respect pedestrians' personal space, decrease speed during close approaches,
and minimize the collision probability with their predicted paths. Unlike
previous DRL methods, our model, designed for AV operation in crowded spaces,
is trained in a novel simulation environment that reflects realistic pedestrian
behaviour in a shared space with vehicles. Results show a 40% decrease in
collision rate and a 15% increase in minimum distance to pedestrians compared
to the state of the art model that does not account for prediction uncertainty.
Additionally, the approach outperforms model predictive control methods that
incorporate the same prediction uncertainties in terms of both performance and
computational time, while producing trajectories closer to human drivers in
similar scenarios.

在人行道密集的环境中，安全、符合社会要求且高效的低速自主车辆的导航需要考虑行人的未来位置以及与车辆和其他人的互动。本研究提出了一种集成预测和规划方法，该方法在模型无关的深度强化学习算法的训练中考虑了预测的行人状态的不确定性。通过引入一种新的奖励函数，该方法促使自主车辆尊重行人的私人空间，在接近行人时减速，并最小化与其预测路径的碰撞概率。结果显示，与不考虑预测不确定性的最先进的模型相比，本方法降低了 40% 的碰撞率，并增加了 15% 的与行人的最小距离。此外，该方法在性能和计算时间方面超过了采用相同预测不确定性的模型预测控制方法，并产生了更接近于人类驾驶员在类似场景中的轨迹。

在共享空间中的自动驾驶车辆群体导航用于不确定性感知的深度强化学习

Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared  Space

Accurate traffic participant prediction is the prerequisite for collision
avoidance of autonomous vehicles. In this work, we predict pedestrians by
emulating their own motion planning. From online observations, we infer a
mixture density function for possible destinations. We use this result as the
goal states of a planning stage that performs motion prediction based on common
behavior patterns. The entire system is modeled as one monolithic neural
network and trained via inverse reinforcement learning. Experimental validation
on real world data shows the system's ability to predict both, destinations and
trajectories accurately.

本文介绍了一种基于自主行车的防撞技术，包括预测行人通过模拟其自身运动规划、推断可能的目的地、规划阶段和行为模式预测的神经网络。实验结果表明，系统能够准确地预测目的地和轨迹。