Abstract
Autonomous vehicles (AVs) are becoming an indispensable part of future transportation. However, safety challenges and lack of reliability limit their real-world deployment. Towards boosting the appearance of AVs on the roads, the interaction of AVs with pedestrians including "prediction of the pedestrian crossing intention" deserves extensive research. This is a highly challenging task as involves multiple non-linear parameters. In this direction, we extract and analyse spatio-temporal visual features of both pedestrian and traffic contexts. The pedestrian features include body pose and local context features that represent the pedestrian's behaviour. Additionally, to understand the global context, we utilise location, motion, and environmental information using scene parsing technology that represents the pedestrian's surroundings, and may affect the pedestrian's intention. Finally, these multi-modality features are intelligently fused for effective intention prediction learning. The experimental results of the proposed model on the JAAD dataset show a superior result on the combined AUC and F1-score compared to the state-of-the-art.
Abstract (translated)
自动驾驶车辆(AVs)已成为未来交通运输不可或缺的部分。然而，安全性挑战和可靠性不足限制了其现实世界的部署。为了增加道路上自动驾驶车辆的出现率，包括“预测行人横穿马路的意图”在内的自动驾驶车辆与行人的互动值得深入研究。这是一个极具挑战性的任务，因为它涉及多个非线性参数。在这方面，我们提取和分析行人和交通场景的时间和空间视觉特征。行人特征包括身体姿势和局部场景特征，代表行人的行为。此外，为了理解全局背景，我们利用场景解析技术利用位置、运动和环境信息，代表行人的周围环境，并且可能影响行人的意图。最后，这些多模态特征进行智能融合，以有效预测意图学习。在JAAD数据集上，提议模型的实验结果显示，与当前最先进的模型相比，其综合AUC和F1得分有更好的表现。
URL
https://arxiv.org/abs/2305.01111
