Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

Islam, Zubayer; Abdel-Aty, Mohamed

doi:10.1177/0361198121990681

Cited by 10 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is observed that the trained model classifies 94% of the training samples with significantly high precision. Compared to studies [2], [16], [17], [18], proposed work outperforms classification of different driving behaviors, in terms of accuracy or data requirements. Figure 3 reflects higher recall for each of the classes in different cross validation folds.…”

Section: B Model Performancementioning

confidence: 85%

“…The study [2] detects lane change with accuracy as low as 58% and high as 97% with various classification algorithms. It must be noted that this study uses Magnetometer, Accelerometer, and Gyroscope sensors besides GPS for data collection.…”

Section: B Model Performancementioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Venkata Ratnam Devanaboyina . lane-change detection [2], behavior detection [3], accident detection [4], traffic event detection [5], user identification [6] through behavioral analyses [7], map generation [8], and outlier detection for location based services (LBS) [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

Rahim

Khan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Whether the computer is driving your car or you are, advanced driver assistance systems (ADAS) come into play on all levels, from weather monitoring to safety. These modern-day ADASs use various assisting tools for drivers to keep the journey safe; these sophisticated tools provide early signals of numerous events, such as road conditions, emerging traffic scenarios, and weather warnings. Many urban applications, such as car-sharing and logistics, rely on accurate and up-to-date road map data. Map generation methods use a variety of data sources, including but not limited to global positioning systems (GPS). In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. It outperforms the existing turn detection schemes on two major frontiers, the required data and the accuracy achieved in detecting different driving behaviors.INDEX TERMS Advance driver assistance systems, CNN, deep learning, GPS data, naturalistic driving, spatio-temporal window analysis, turn detection.

show abstract

Section: B Model Performancementioning

confidence: 85%

Section: B Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

Rahim

Khan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Other road geometries can also be used to test the synthesized traffic monitors under different conditions. Such monitors could also be extended to study more complex traffic interactions such as lane changes ( 40 , 41 ). Finally, online monitoring is the natural next step for the synthesis of real-time monitors.…”

Section: Discussionmentioning

confidence: 99%

Toward Formalization and Monitoring of Microscopic Traffic Parameters Using Temporal Logic

Nour

Zaki

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Smart mobility is transforming the way the current transportation network is envisioned. It holds the promise of a more sustainable, safer, and efficient future for commuters. Nevertheless, traffic management centers are constantly facing the challenges of ensuring that transportation system components are operating as expected and in a safe manner. As a result, research efforts on improving traffic monitoring aim to design and implement novel approaches for safety applications. In this paper, we adopt formal methods to specify and apply reason to the traffic network’s complex properties. Formal methods provide a framework to rigorously define the safe operation of the traffic network by capturing non-conforming travel behavior, exploring various possible states of vehicular traffic, and detecting any irregularities that may arise. In this work, a new approach to traffic monitoring is proposed, which uses specification-based monitoring. We develop monitors that define traffic parameters, such as conforming to speed limits and maintaining appropriate headway. A formal language known as Signal Temporal Logic is used to specify and analyze these traffic rules. The proposed framework is then applied to a calibrated micro-simulated highway network to identify whether individual vehicle trajectories violate or satisfy the proposed specifications. Statistical analysis of the outputs shows that our proposed approach is effective in differentiating between violating and conforming vehicles. This approach can be used by traffic management centers that are seeking to accommodate emerging mobility technologies that are autonomous and connected. In particular, the presented work can be valuable in studying traffic stream properties, identifying possible hazards, and providing valuable feedback for automating traffic monitoring systems.

show abstract

“…Various smartphone sensors were used by previous studies ( 4 , 9 , 18 – 20 ), including GPS, accelerometer, and gyroscope. This paper selects accelerometer and gyroscope because of their popularity and the consideration of battery consumption.…”

Section: Research Approachmentioning

confidence: 99%

Driving Maneuvers Detection using Semi-Supervised Long Short-Term Memory and Smartphone Sensors

Abdel-Aty

Islam

2021

Transportation Research Record

Self Cite

View full text Add to dashboard Cite

Driving maneuvers detection is an important component of proactive traffic safety management and connected vehicle systems. Most of the existing studies used supervised learning concepts to train their models with labeled data. These methods achieved promising results but were limited by the heavy dependence on the labeled data. With the development of mobile sensing technologies, massive traffic-related data can be efficiently collected by mobile devices (e.g., smartphones, tablets, etc.). Considering the high costs of labeling data, this paper proposed a semi-supervised deep learning method to learn from the unlabeled data. Data from a smartphone’s accelerometer and gyroscope were collected by different drivers with a variety of smartphones, vehicles, and locations. Three long short-term memory (LSTM) models were trained with the proposed semi-supervised learning algorithm. Experimental results indicated that the proposed semi-supervised LSTM could learn from the unlabeled data and achieve outstanding results with only a small portion of the labeled data. Using much fewer labeled data, semi-supervised LSTM could achieve similar results compared with the supervised method. Moreover, the proposed method outperformed other machine learning methods (e.g., convolutional neural network, XGBoost, random forest) on precision, recall, F1-score, and area under curve. More and more traffic data will be available in the future, the proposed method is expected to make use of the undiscovered potential from the massive unlabeled data.

show abstract

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

Cited by 10 publications

References 34 publications

A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

Toward Formalization and Monitoring of Microscopic Traffic Parameters Using Temporal Logic

Driving Maneuvers Detection using Semi-Supervised Long Short-Term Memory and Smartphone Sensors

Contact Info

Product

Resources

About