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.

翻译：自动驾驶汽车正成为未来交通不可或缺的一部分。然而，安全挑战和可靠性不足限制了其在实际场景中的部署。为推动自动驾驶汽车上路，其与行人的交互（包括“行人过街意图预测”）值得深入研究。这是一项极具挑战性的任务，涉及多个非线性参数。为此，我们提取并分析了行人与交通环境的时空视觉特征。行人特征包括表征其行为的身体姿态和局部上下文特征。同时，为理解全局上下文，我们利用场景解析技术获取位置、运动和环境信息，以表征行人周边可能影响其意图的交通环境。最终，这些多模态特征被智能融合以实现有效的意图预测学习。在JAAD数据集上的实验结果表明，与现有最先进方法相比，所提模型的AUC与F1-score综合指标表现更优。