VIA NOVA presents two journal papers at TRB

3 februari 2020

For the second time, the VIA NOVA team presents a journal paper at the prestegious Transportation Research Board (TRB) annual meeting which is a part of National Academies of Science Engineering and Medicine. 

In January 2020, the paper titled “Identifying Road Anomalies using in-vehicle sensors for cooperative mobility applications and road asset management” was presented in the 99th annual meeting at Washington D.C. The authors of this paper were Moksheeth Padarthy, Mohammed Sami and Emiliano Heyns. 

Paper Abstract:

One of the main challenges for the road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the health of the vehicles and the occupants of the vehicle. Strategies such as Pavement Management Systems use Pavement Evaluation Vehicles that are equipped with state-of-the-art devices to assist the road authorities to repair these anomalies. Due to the limited availability of such vehicles, however, the quantity of data that is available is limited to the coverage of these vehicles. To address this problem, several investigations have been conducted on using smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in passenger vehicles to identify road anomalies. This empowers the road authorities with additional moving sensors (vehicles travelling on a particular road) that provide an initial insight into the presence of anomalies. This information could then be used to assist road authorities to precisely deploy their staff and equipment at these locations such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speeds signals, which are commonly available vehicular variables was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with actual test scenarios.

The first journal paper with the title "Identification and classification of slippery winter road conditions using commonly available in-vehicle variables" was presented in January 2019 at Washington D.C. The authors of this paper were Moksheeth Padarthy and Emiliano Heyns. The abstract of the paper follows: 

Paper Abstract:

Extreme winter weather conditions severely affect the transportation sector. Technologies such as Road Weather Information Systems provide live data regarding the road surface conditions to assist the road authorities in providing safe mobility. The main problem is, however, the limited number of such systems that have been deployed resulting in fragmented information about the road conditions.This paper addresses the problem associated with the limited quantity of information concerning slippery winter road conditions by presenting a proof-of-concept of a system that not only detects slippery winter road conditions but also predicts the type of slippery surface (ice, snow and slush) via vehicle-based systems. The concept demonstrated in this paper makes use of commonly available variables, which are, longitudinal slip ratios, longitudinal acceleration and the ambient temperature to identify such situations. The developed system employs a Fuzzy Inference System that is not only capable of identifying slippery conditions but is also capable of classifying surfaces based on the extent of slipperiness. This empowers the road authorities with several moving sensors (vehicles traveling on a particular road) compared to a few fixed sensors it currently has. This could deliver a pool of information assisting the road authorities to efficiently handle their staff and equipment such that appropriate equipment reaches the right place at the right time.

Bron: HAN Automotive Research