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Multiple Event-Based Simulation Scenario Generation Approach for Autonomous Vehicle Smart Sensors and Devices

机译：自主车辆智能传感器和设备的基于多事件的仿真场景生成方法

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摘要

Nowadays, deep learning methods based on a virtual environment are widely applied to research and technology development for autonomous vehicle’s smart sensors and devices. Learning various driving environments in advance is important to handle unexpected situations that can exist in the real world and to continue driving without accident. For training smart sensors and devices of an autonomous vehicle well, a virtual simulator should create scenarios of various possible real-world situations. To create reality-based scenarios, data on the real environment must be collected from a real driving vehicle or a scenario analysis process conducted by experts. However, these two approaches increase the period and the cost of scenario generation as more scenarios are created. This paper proposes a scenario generation method based on deep learning to create scenarios automatically for training autonomous vehicle smart sensors and devices. To generate various scenarios, the proposed method extracts multiple events from a video which is taken on a real road by using deep learning and generates the multiple event in a virtual simulator. First, Faster-region based convolution neural network (Faster-RCNN) extracts bounding boxes of each object in a driving video. Second, the high-level event bounding boxes are calculated. Third, long-term recurrent convolution networks (LRCN) classify each type of extracted event. Finally, all multiple event classification results are combined into one scenario. The generated scenarios can be used in an autonomous driving simulator to teach multiple events that occur during real-world driving. To verify the performance of the proposed scenario generation method, experiments using real driving video data and a virtual simulator were conducted. The results for deep learning model show an accuracy of 95.6%; furthermore, multiple high-level events were extracted, and various scenarios were generated in a virtual simulator for smart sensors and devices of an autonomous vehicle.
机译：如今，基于虚拟环境的深度学习方法已广泛应用于自动驾驶汽车的智能传感器和设备的研究和技术开发。提前学习各种驾驶环境对于处理现实世界中可能出现的意外情况以及继续无事故驾驶至关重要。为了训练自动驾驶车辆的智能传感器和设备，虚拟模拟器应创建各种可能的现实情况的方案。要创建基于现实的场景，必须从真实的驾驶车辆或专家进行的场景分析过程中收集有关真实环境的数据。但是，随着创建更多方案，这两种方法会增加方案生成的时间和成本。本文提出了一种基于深度学习的场景生成方法，可以自动创建用于训练自动驾驶智能传感器和设备的场景。为了生成各种场景，所提出的方法通过使用深度学习从在真实道路上拍摄的视频中提取多个事件，并在虚拟模拟器中生成多个事件。首先，基于快速区域的卷积神经网络（Faster-RCNN）提取驾驶视频中每个对象的边界框。第二，计算高级事件边界框。第三，长期递归卷积网络（LRCN）对提取事件的每种类型进行分类。最后，将所有多个事件分类结果组合到一个场景中。生成的场景可用于自动驾驶模拟器中，以教授在现实驾驶中发生的多个事件。为了验证所提出的场景生成方法的性能，进行了使用真实驾驶视频数据和虚拟模拟器的实验。深度学习模型的结果显示准确率为95.6％；此外，提取了多个高级别事件，并在虚拟模拟器中为自动驾驶车辆的智能传感器和设备生成了各种场景。

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