Gupta, Shobhit (The Ohio State University), Rajakumar Deshpande, Shreshta (The Ohio State University), Tulpule, Punit (The Ohio State University), Rizzoni, Giorgio (Ohio State Univ), Canova, Marcello (The Ohio State University)

An Enhanced Driver Model for Evaluating Fuel Economy on Real-World Routes

Scheduled for presentation during the Invited Session "New Advances in Control of HEV Powertrains" (WeCT3), Wednesday, June 26, 2019, 16:50−17:10, Chamerolles

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

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.