A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information
A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information
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DOI：
10.1111/mice.12495
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发表时间：
2019-09-01
影响因子：
9.6
通讯作者：
Labi, Samuel
中科院分区：
文献类型：
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作者：
Chen, Sikai;Leng, Yue;Labi, Samuel
自动驾驶汽车（AV）利益相关者继续通过在运营道路、AV专用道路网络和AV测试轨道上进行AV测试来确保这项新技术的安全性能。然而，最近在服务道路上与AV相关的死亡事件加剧了公众的怀疑，并削弱了公众对AV操作安全性的信任。此外，测试轨道无法充分表征真实世界的驾驶环境。出于这个原因，驾驶模拟器继续作为一个有吸引力的AV测试手段。然而，在大多数AV驾驶模拟器中，AV操作基于车辆外部的命令，并嵌入驾驶环境的代码中。为了解决与这种方法相关的模拟不足，本文开发了一种用于自动驾驶模拟的深度卷积神经网络-长短期记忆（CNN-LSTM）算法。该算法通过观察和表征无人驾驶汽车的驾驶环境，控制无人驾驶汽车在驾驶模拟中的运动。CNN部分提取使用迁移学习引入人类先验知识的特征，LSTM部分使用时间信息来处理提取的特征，并结合时间动态来预测驾驶决策。AV还可以使用具有包含道路环境数据的数据库的外部服务器作为附加信息源。众所周知，不同的驾驶模拟器在其功能和访问驾驶环境数据的能力方面有所不同。因此，为了使它足够灵活，以方便复制的其他研究人员使用驾驶模拟器，该算法已被设计和证明，仅使用图像数据的驾驶环境作为输入。这是因为道路图像数据可以从任何驾驶模拟器的屏幕上轻松访问。所提出的算法进行了测试，使用开放的赛车模拟器测试轨道平台，并被发现能够模仿人类驾驶决策的高度准确性。
Autonomous vehicle (AV) stakeholders continue to seek assurance of the safety performance of this new technology through AV testing on in-service roads, AV-dedicated road networks, and AV test tracks. However, recent AV-related fatalities on in-service roads have exacerbated public skepticism and eroded some public trust in the safety of AV operations. Further, test tracks are unable to characterize adequately the real-world driving environment. For this reason, driving simulators continue to serve as an attractive means of AV testing. However, in most AV driving simulators, the AV operation is based on commands external to the vehicle and embedded in the code for the driving environment. To address the simulation shortfalls associated with this approach, this paper develops a deep convolutional neural network-long short-term memory (CNN-LSTM) algorithm for self-driving simulation. This algorithm observes and characterizes the AV's driving environment, and controls the AV movement in the driving simulation. The CNN part extracts features that use transfer learning to introduce human prior knowledge, and the LSTM part uses temporal information to process the extracted features, and incorporates temporal dynamics to predict driving decisions. The AV may also use an external server with a database containing road environment data as an additional source of information. It is acknowledged that different driving simulators differ in their functions and their capabilities to access driving-environment data. Therefore, to make it sufficiently flexible to facilitate replication by other researchers that use driving simulators, the algorithm has been designed and demonstrated using only image data of the driving environment as input. This is because roadway image data are easily and readily accessible from the screen of any driving simulator. The proposed algorithm was tested using the open racing car simulator test track platform and was found to be able to mimic human driving decisions with a high degree of accuracy.