Generative AI Based Methodology for Road Profile Mix Creation Ashok Leyland, Limited

Format:

Book

Conference/Event

Author/Creator:

Rajappan, Dinesh Kumar, author.

Contributor:

N, Gopi Kannan

R, Suresh

Venkatesh, Anirudh Anand

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

The acquisition of road profile data is crucial for various automotive testing applications, including vehicle dynamics analysis, chassis endurance tests, and simulation of vehicle-road interactions. This is necessary for conducting virtual tests to accelerate research and development processes and can significantly reduce testing costs. However, most of the on-road measurements lack comprehensive and relevant road profile data. Conducting on-road trials to acquire this data is a laborious and time-consuming process, often impeded by logistical and environmental challenges. This research proposes a generative AI-based methodology for creating diverse and realistic road profile mixes from the existing on-road dataset of front axle displacement and road profile measured with a laser sensor. By leveraging advanced machine learning techniques, the proposed approach seeks to generate synthetic road profiles that accurately reflect real-world conditions, thereby reducing the dependency on extensive on-road measurements. The methodology involves classifying the existing road profile data into respective road classes (as per ISO 8608:2016) and training a random forest classification model based on the data. Further, this model is then used to train a conditional Generative Adversarial Network (cGAN) on this dataset to generate a synthetic road profile which shares the same statistical properties of the original training dataset. In addition to addressing the physical constraints of on-road data acquisition, this methodology serves to enhance the capability of simulating and analyzing the vehicle-to-road interactions under diverse conditions

Notes:

Vendor supplied data

Publisher Number:

2026-26-0674

Access Restriction:

Restricted for use by site license