智能科学与技术学报 ›› 2023, Vol. 5 ›› Issue (2): 180-199.doi: 10.11959/j.issn.2096-6652.202317
黄峻1,2,3, 田永林1,4, 戴星原1,5, 王晓3,6,7,8, 平之行4,5,9
修回日期:
2023-05-26
出版日期:
2023-06-15
发布日期:
2023-06-10
作者简介:
黄峻(1998- ),男,中国科学院自动化研究所复杂系统管理与控制国家重点实验室轨迹规划工程师,主要研究方向为智能交通系统、深度学习、强化学习基金资助:
Jun HUANG1,2,3, Yonglin TIAN1,4, Xingyuan DAI1,5, Xiao WANG3,6,7,8, Zhixing PING4,5,9
Revised:
2023-05-26
Online:
2023-06-15
Published:
2023-06-10
Supported by:
摘要:
对周围车辆轨迹的精确预测可以辅助自动驾驶车辆做出合理的即时决策。虽然相比传统轨迹预测算法,深度学习方法已取得较好效果,但是自动驾驶车辆在异构高动态复杂变化环境下实现多模态高精度预测仍存在信息丢失、交互和不确定性难以建模、预测缺乏可解释性等问题。Transformer具备的长距离建模能力和并行计算能力使其不仅在自然语言处理领域取得巨大成功,而且在扩展至自动驾驶多模态轨迹预测任务时也解决了以上问题。基于此,对过去基于深度神经网络的方法,特别是对基于Transformer的方法进行全面总结与回顾;同时分析了Transformer相较于传统序列网络、图神经网络、生成模型的优势,并结合现有难题进行针对性分析与分类。Transformer模型可以更好地应用于多模态轨迹预测任务,此类模型具有更好的泛化性和可解释性。最后,对多模态轨迹预测未来发展方向进行了展望。
中图分类号:
黄峻, 田永林, 戴星原, 等. 基于深度学习的自动驾驶多模态轨迹预测方法:现状及展望[J]. 智能科学与技术学报, 2023, 5(2): 180-199.
Jun HUANG, Yonglin TIAN, Xingyuan DAI, et al. Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 180-199.
表1
用于自动驾驶轨迹预测任务的数据集"
数据集 | 年份 | 交通参与者 | 传感器 | 场景 | 数据内容 | 基于此数据集的算法 |
NuScenes[ | 2020年 | 车辆、行人、自行车 | 雷达、相机 | 城市 | 驾驶轨迹、高清地图 | MHA-JAM |
Lyft[ | 2020年 | 车辆、行人、自行车 | 雷达、相机 | 城市 | 驾驶轨迹、高清地图 | Gatformer |
Waymo[ | 2020年 | 车辆、行人、自行车 | 雷达、相机 | 城市 | 驾驶轨迹、高清地图 | Wayformer、DenseTNT |
Argoverse[ | 2019年 | 车辆 | 雷达、相机 | 无信号灯路口 | 驾驶轨迹、高清地图 | mmTransformer、HOME、TrajFormer、VertorNet |
INTERACTION[ | 2019年 | 车辆、行人 | 无人机、相机 | 高速公路、无信号灯路口、有信号灯路口、环岛 | 驾驶轨迹、高清地图 | GOHOME、THOMAS、TNT |
HighD[ | 2018年 | 车辆 | 无人机 | 城市路口 | 驾驶轨迹、车道 | MHA-LSTM |
Apolloscape[ | 2018年 | 车辆、行人、自行车 | 雷达、相机 | 城市高速公路 | 驾驶轨迹 | AI-TP、S2TNet、GRIP++ |
KITTI[ | 2013年 | 行人、自行车 | 雷达、相机 | 城市高速公路 | 图像、点云 | DESIRE |
NGSIM | 2006年 | 车辆 | 相机 | 高速公路路口 | 驾驶轨迹、车道 | Maneuver based LSTM、GRIP++ |
表2
多模态轨迹预测问题及基于Transformer模型的解决办法"
研究问题 | 解决办法 |
时间、空间维度编码问题: | mmTransformer:通过使用3个堆叠Transformer对环境信息、历史轨迹及车辆特征信息分别编码; |
时间维度、空间维度信息如何编码? | LAformer:时间密集型多模态轨迹预测模型,通过两阶段方法生成随时间变化、可调整的多模态轨迹并建模 |
是否会有信息丢失? | 交互; |
AgentFormer:时间编码器代替位置编码器,通过时间戳保留时间信息 | |
交互问题(自注意力计算): | mmTransformer:对交互信息编码,对车辆间依赖关系建模; |
智能体间交互如何建模? | AgentFormer:Agent-Aware机制分别计算智能体间和智能体自身内在注意力; |
有什么样的依赖关系? | TrajFormer:通过自注意力解决提取局部特征受限问题,更好地提取环境交互信息; |
是否有交互信息丢失问题?如何解决? | MPTP:通过补丁合并和移位窗口方法降低自注意力计算复杂性; |
自注意力计算效率如何提升? | Gatformer:对智能体-智能体、智能体-环境交互建模 |
Spatio-Channel TF:通过基于挤压激励网络的Channel模块捕获相邻Channel之间的相互作用,进而捕获多智 | |
多智能体交互问题: | 能体间的交互信息; |
多智能体交互信息如何捕获? | GatFormer:采用灵活的图结构,降低计算复杂度,通过交互权重分配提高性能和模型的可解释性,同时保证 |
鲁棒性 | |
可解释性问题: | |
可解释性差的原因? | MPTP:Swim Transformer编码环境信息,GAR编码历史轨迹,生成可解释性高的预测轨迹 |
如何提高可解释性? | |
规模化问题: | |
规模化难的原因? | Wayformer:分析前融合、后融合、分层融合的利弊,为TF如何扩展到大型多维序列提供解方案 |
编码模块如何优化? |
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