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Drive Scenario Generation Based on Metrics for Evaluating an Autonomous Vehicle Controller
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
An important part of automotive driving assistance systems and autonomous vehicles is speed optimization and traffic flow adaptation. Vehicle sensors and wireless communication with surrounding vehicles and road infrastructure allow for predictive control strategies taking near-future road and traffic information into consideration to improve fuel economy. For the development of autonomous vehicle speed control algorithms, it is imperative that the controller can be evaluated under different realistic driving and traffic conditions. Evaluation in real-life traffic situations is difficult and experimental methods are necessary where similar driving conditions can be reproduced to compare different control strategies. A traditional approach for evaluating vehicle performance, for example fuel consumption, is to use predefined driving cycles including a speed profile the vehicle should follow. However, if the vehicle speed is part of the vehicle control output, a different vehicle evaluation framework is necessary. Here, speed constraints are defined based on route and traffic conditions, such as speed limits, traffic signs and signals, and the locations of surrounding vehicles. Hence, route generation is an important task for evaluating speed control algorithms. A route is a distance-based description of the road conditions and locations of traffic signs and signals. A driving scenario is defined as a route which also includes information about traffic density and the location of surrounding traffic as function of time. It is discussed how driving scenarios can be used to evaluate and compare different speed control algorithms. The generation of driving scenarios is performed in two steps, route generation and traffic data generation. First, two approaches are discussed for generating the route conditions, such as varying speed limits and locations of traffic signals, either using real road map data or to recreate from vehicle speed data. In a second step, traffic conditions are simulated using the software SUMO to generate speed profiles of surrounding vehicles on the road. To assure that the selected driving scenarios represent varying driving conditions, a set of metrics is selected and used for driving scenario selection.
CitationTamilarasan, S., Jung, D., and Guvenc, L., "Drive Scenario Generation Based on Metrics for Evaluating an Autonomous Vehicle Controller," SAE Technical Paper 2018-01-0034, 2018, https://doi.org/10.4271/2018-01-0034.
Data Sets - Support Documents
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