| 题名 | 面向无人驾驶的数据驱动行为决策和轨迹规划 |
| 其他题名 | DATA-DRIVEN MANEUVERING AND TRAJECTORY PLANNING FOR AUTONOMOUS DRIVING
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| 姓名 | |
| 姓名拼音 | GAO Rui
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| 学号 | 12032493
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| 学位类型 | 硕士
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| 学位专业 | 0809 电子科学与技术
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| 学科门类/专业学位类别 | 08 工学
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| 导师 | 郝祁 
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| 导师单位 | 计算机科学与工程系
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| 论文答辩日期 | 2023-05-13
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| 论文提交日期 | 2023-06-27
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| 学位授予单位 | 南方科技大学
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| 学位授予地点 | 深圳
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| 摘要 | 自动驾驶局部决策规划作为自动驾驶决策规划模块的重要组成部分，是自动驾驶系统的重要研究方向之一。优秀的决策规划算法能保证安全舒适，解决自动驾驶的安全性问题，能极大的推动自动驾驶的发展。传统决策规划算法难以处理动态障碍物多的场景且对环境变化敏感。环境动态障碍物增多或环境发生变化，都可能导致问题难以求解。近年来，随着深度学习的发展，大量的驾驶数据被收集，为使用深度学习处理决策规划问题提供了理论依据和数据支撑。现有的基于深度学习的局部决策规划算法在以下三个方面存在不足：（1）现有决策规划算法难以捕捉自车和行人之间的交互关系导致泛化效果差；（2）在行为决策建模中忽略了不同的驾驶风格，学习到的决策模型环境适应性差；（3）轨迹规划网络生成轨迹的可行性和安全性难以得到保证。为了处理上述问题，（1）本文提出了拥挤场景下基于强化学习的运动规划算法，使用无模型的强化学习算法在仿真环境中交互学习决策规划策略，设计了自适应感知网络和基于注意力机制动态环境特征提取网络捕捉自车和行人之间的交互关系，并且设计了基于危险感知的奖励函数；（2）为了学习不同的驾驶风格，本文使用模仿学习来学习决策模型，并且设计了基于变分自编码器的多模态决策模型，使用改进后的变分自编码器学习不同的驾驶风格并且结合周围车辆的轨迹预测信息生成自车不同驾驶风格的行为决策；（3）考虑到网络生成轨迹的可行性和安全性，本文设计了基于注意力机制轨迹规划网络，采用了注意力机制融合时序信息和决策信息，并且设计了可微分的碰撞损失函数和基于模型预测控制可微分优化层约束轨迹使其安全可行；基于本文所提出的方法，本文设计了无人驾驶局部决策-规划系统，并将其部署到自动驾驶仿真器中，为后续的研究提供了支撑。 |
| 其他摘要 | Autonomous driving (AD) local decision-making planning, as an important compo nent of AD decision-making planning module, is one of the important research areas of AD systems. Excellent decision-making planning algorithms can ensure vehicular safety and comfort, increase the system robustness, and greatly promote the development of AD applications. Traditional decision planning algorithms are challenged with scenarios that have many dynamic obstacles and are sensitive to environmental changes. An increase in dynamic obstacles or changes in the environment can lead to difficulties in problem solving. In recent years, with the development of deep learning, a large amount of driving data has been collected, providing theoretical basis and data support for using deep learn ing to process decision-making planning problems. Existing deep learning-based local decision-making planning algorithms have the following three shortcomings: (1) The current decision planning algorithms are difficult to capture the interaction between the ego vehicle and pedestrians, resulting in poor gen eralization performance; (2) Different driving styles are ignored in behavioral decision making modeling, and the learned decision-making model has poor environmental adapt ability; (3) The feasibility and safety of trajectory planning network-generated trajectories are difficult to guarantee. The contributions of this thesis include the following three aspects: (1) This thesis proposes a reinforcement learning-based obstacle avoidance algo rithm in crowded scenes, using model-free reinforcement learning algorithms to interac tively learn decision-making planning strategies in a simulation environment, developing adaptive perception networks and attention-based dynamic environment feature extrac tion networks to handle unpredictable dynamic environments, as well as a risk-aware re ward function; (2) To learn different driving styles, this thesis uses imitation learning to learn decision-making models and designs a multi-modal decision-making model based on variational autoencoder. Using an improved variational autoencoder, it learns differ ent driving styles and combines with the trajectory prediction information of surrounding vehicles to generate behavior decisions for the autonomous vehicle with different driv ing styles; (3) Considering the feasibility and safety of network-generated trajectories, this thesis deveops an attention-based trajectory planning network, which uses attention mechanisms to fuse temporal and decision-making information and embeds model pre II Abstract dictive control into differentiable optimization layers to jointly constrain trajectories to make them smooth. Based on the proposed methods, this thesis develops an autonomous driving local decision-making planning system, deploys it to an autonomous driving sim ulator, and provides solid supports for the future research. |
| 关键词 | |
| 语种 | 中文
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| 培养类别 | 独立培养
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| 入学年份 | 2020
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| 学位授予年份 | 2023-06
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| 所在学位评定分委会 | 电子科学与技术
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| 国内图书分类号 | TP242.6
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| 来源库 | 人工提交
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| 成果类型 | 学位论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/544074 |
| 专题 | 工学院_计算机科学与工程系 |
| 推荐引用方式 GB/T 7714 |
高瑞. 面向无人驾驶的数据驱动行为决策和轨迹规划[D]. 深圳. 南方科技大学,2023.
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