复杂交互场景下面向自动驾驶的车辆轨迹预测研究

近年来，自动驾驶汽车因其在保障安全出行、缓解城市交通堵塞问题上有着巨大的潜力，得到了工业界和学术界的广泛关注。发展自动驾驶技术的一个主要目的就是减少致死交通事故的发生。为了能够预知环境中潜在的危险以及做出长时间内的最优决策，自动驾驶汽车需要对周围车辆的未来运动做出准确的预测。本文针对复杂交互场景中的车辆轨迹预测问题进行研究，旨在为下游的决策规划模块提供准确合理的先验信息。汽车的未来运动通常受到场景语境信息（Scene Context）（如道路拓扑结构、交通规则、车辆种类和密度）以及车辆与车辆、车辆与环境之间的交互行为的影响，因此如何充分利用场景语境信息和复杂交互信息来生成符合车辆运动学约束的预测轨迹仍然是一个挑战。
为了解决上述问题，本文提出了一个数据和模型联合驱动的车辆轨迹预测方法（Prediction with Interaction and Scene Context，PRISC-Net），它不仅能够同时利用运动规划模型和深度学习方法的优点对目标车辆的未来运动做出准确、合理的预测，还可以输出预测过程中不同阶段的预测结果，便于后续调试以及改进。本文的创新点有以下三点：（1）提出了一个场景语境和交互感知的候选目标点预测方法，它能够从显式（如道路结构）和隐式（如交通规则、限速等）的场景语境信息以及车辆历史状态信息中提取出有效的场景语境特征以及车辆与车辆之间的交互特征，对目标车辆的未来最终位置进行准确且合理的预测；（2）提出了一个基于优化和运动规划模型的轨迹生成方法，它能够克服现有方法的缺点，生成高质量的可行候选轨迹；（3）将提出的候选目标点预测方法和轨迹生成方法与交互感知的轨迹筛选方法结合起来，以捕捉复杂的车辆与车辆以及车辆与环境之间的交互行为，并输出准确合理的预测轨迹。在真实和仿真车辆轨迹数据集上的实验结果表明了相对于现有的主流方法，PRISC-Net 能够输出更加准确和合理的预测结果。此外，本文也搭建了一个包括车辆轨迹数据自动收集工具和实时预测系统的完备仿真测试平台，便于轨迹预测算法的研究及测试。

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