In this endeavor, we developed a comprehensive system that processes integrated visual features derived from video frames captured by a regular camera, along with depth details obtained from a point cloud scanner. This system is designed to anticipate driving actions, encompassing both vehicle speed and steering angle. To ensure its reliability, we conducted assessments where we juxtaposed the projected outcomes with the established norms adhered to by skilled real-world drivers. Our evaluation outcomes indicate that the forecasts achieve a noteworthy level of accuracy in a minimum of half the test scenarios (ranging around 50-80%, contingent on the specific model). Notably, the utilization of amalgamated features yielded superior performance in comparison to using video frames in isolation, as demonstrated by most of the cases.

翻译：本研究开发了一个综合系统，用于处理由常规摄像头捕获的视频帧中提取的集成视觉特征，以及从点云扫描仪获取的深度信息。该系统旨在预测车辆速度和转向角度等驾驶行为。为确保其可靠性，我们通过将预测结果与经验丰富的现实驾驶员遵循的既定规范进行对比评估。评估结果表明，在至少半数测试场景中（具体模型表现约为50-80%），预测达到了显著准确度。值得注意的是，大多数案例显示，相较于单独使用视频帧，融合特征的应用带来了更优性能。