Tracking and climbing behavior recognition of heavy-duty trucks on roadways

Lei Tang,Xin Wang,Weiwei Kong,Jingyu Ma,Jingchi Jia,Zongtao Duan

doi:10.1177/1550147720903620

Abstract

The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis–Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically.

Highlights

Coasting and braking on long inclined roadways are one of the primary reasons for traffic accidents for heavy-duty trucks.[1,2,3] To improve truck climbing, we have to track and accurately predict the trucks’ movements while driving uphill and downhill
To extract the relative elevation difference (rED) and EDS, we first model changes in the elevation between two consecutive trajectory points and obtain the duration for which a truck travels with the same type of motion, such as where the truck is continuously ascending or descending over a given time interval
We considered the climbing behavior of heavy-duty trucks as a movement from a level roadway to an inclined roadway

Summary

Introduction

Coasting and braking on long inclined roadways are one of the primary reasons for traffic accidents for heavy-duty trucks.[1,2,3] To improve truck climbing, we have to track and accurately predict the trucks’ movements while driving uphill and downhill. The positioning error is 2.5 m that enables to fulfill the requirements for the high-sampling-rate trajectory Each record of this dataset contains geographical location in the form of latitude and longitude, elevation, velocity, and time at each instance of heavy-duty truck activity, which includes traveling both up and down the slopes. This work aims to provide a model that can support both the generation of origin-destination trips related to elevation and the identification of probability distributions for the motions of a heavy-duty truck for a particular period of a day based on the passive data employed. To extract the rED and EDS, we first model changes in the elevation between two consecutive trajectory points and obtain the duration for which a truck travels with the same type of motion, such as where the truck is continuously ascending or descending over a given time interval.

10. EDS EDSk

Findings

Experimental setup