In highway scenarios, an alert human driver will typically anticipate early cut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly. Autonomous vehicles must anticipate these situations at an early stage too, to increase their safety and efficiency. In this work, lane-change recognition and prediction tasks are posed as video action recognition problems. Up to four different two-stream-based approaches, that have been successfully applied to address human action recognition, are adapted here by stacking visual cues from forward-looking video cameras to recognize and anticipate lane-changes of target vehicles. We study the influence of context and observation horizons on performance, and different prediction horizons are analyzed. The different models are trained and evaluated using the PREVENTION dataset. The obtained results clearly demonstrate the potential of these methodologies to serve as robust predictors of future lane-changes of surrounding vehicles proving an accuracy higher than 90% in time horizons of between 1-2 seconds.
翻译:在高速公路情景中,驾驶警示的驾驶员通常会主要通过视觉提示来预测周围车辆的早期切入/切出动作。自治车辆也必须在早期阶段预测这些情况,以提高其安全和效率。在这一工作中,车道变化的识别和预测任务被作为视频动作识别问题提出来。在成功用于解决人类行动识别的四种不同的双流方法中,通过从前视摄像头中堆放视觉提示来调整,以识别和预测目标车辆的车道变化。我们研究了背景和观测前景对性能的影响,分析了不同的预测前景。不同的模型是利用预防数据集进行培训和评价的。所获得的结果清楚地表明了这些方法作为未来车道变化的稳健预测器的潜力,证明在1-2秒之间的时空前景中准确度高于90%。