Driving Scenario Design for Driving Simulation Experiments Based on Sensor Trigger Mechanism

Xu, Yongcun; Yan, Xuedong; Liu, Dan; Xiang, Wang

doi:10.3923/itj.2012.420.425

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…Several driving simulators has been investigated for development and verification of an autonomous vehicle. The field of driving scenario generation for the operation of autonomous vehicles has recently drawn substantial attention [11,12,13,14,15,16]. Research on driving scenario generation is largely classified into model-based and data-based scenario generation.…”

Section: Related Workmentioning

confidence: 99%

“…The model-based scenario generation approach defines driving elements, including traffic lane, car, pedestrian, and accident events, in advance as well as scenarios depending on those elements. In [11] the authors plan movements and generate scenarios by using the action tree of each car based on the accident scenario defined in scripts, and the research in [12] predefines the accident scenario between a car and a pedestrian in the intersection and generates the scenarios. In [13] the authors generate the scenarios based on an analysis of real car accidents and survey data from ‘NMVCCS’ and in [14] the authors implement the ontology on the driving environment and generate scenarios based on that ontology.…”

Section: Related Workmentioning

confidence: 99%

“…Existing studies are based on the scenario generation approaches for autonomous vehicle generated scenarios based on real driving data acquired from the real environment or by using self-driving scenario generation modeling based on expert knowledge [11,12,13,14,15,16]. However, such approaches require a high ratio of manual processing, which increases the development costs and time for the self-driving simulator.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Park

Wen

Sung

et al. 2019

Sensors

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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.

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