An Enhanced Driver Model for Evaluating Fuel Economy on Real-World Routes
| Autor: | Marcello Canova, Shobhit Gupta, Giorgio Rizzoni, Punit Tulpule, Shreshta Rajakumar Deshpande |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Economy SIMPLE (military communications protocol) Control and Systems Engineering Computer science 020208 electrical & electronic engineering 0202 electrical engineering electronic engineering information engineering Intelligent driver model Advanced driver assistance systems 02 engineering and technology Baseline (configuration management) |
| Zdroj: | IFAC-PapersOnLine. 52:574-579 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2019.09.091 |
| Popis: | Assessing vehicle fuel economy in real-world driving conditions is a critical requirement to establish a reliable baseline when evaluating driver assistance systems or autonomous vehicles, where the speed profile can be optimized based on route information. Since the benchmarking is traditionally done by collecting and analyzing large amounts of data over on-road testing, virtual driver models have been developed to conduct simulation studies that allow one to understand the impact of specific driver behaviors on the vehicle speed profile. This paper presents an enhanced driver model that predicts a longitudinal vehicle speed profile based on route data, which can be calibrated with simple tests. The model extends the Intelligent Driver Model to more accurately characterize the response to stop signs, traffic lights, and other conditions typical of urban driving. The enhanced driver model can be calibrated to match the behavior of specific drivers and determine statistically-relevant distributions of model parameters. |
| Databáze: | OpenAIRE |
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