Modeling tactical lane-change behavior for automated vehicles: A supervised machine learning approach

Motamedidehkordi, Nassim; Amini, Sasan; Hoffmann, Silja; Busch, Fritz; Fitriyanti, Mustika Riziki

doi:10.1109/mtits.2017.8005678

Cited by 32 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [255], SVM was used to classify the driver's intention of lane changing. The authors in [256] compared the accuracy performance of various supervised learning approaches, such as SVM, naive Bayes, logic regression, nearest neighborhoods, decision trees, extra trees, and random forest classifiers, in lane changing modeling.…”

Section: ) Lane Changing Assessmentmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

View full text Add to dashboard Cite

Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

show abstract

Section: ) Lane Changing Assessmentmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Dou et al [11] considered mandatory lane change events at lane drops and predicted driver merging behavior with SVMs and simple Feedforward Neural Networks. Different algorithms were compared by Motamedidehkordi et al [2] for solving the same problem of learning human driving behavior and predicting lane change decisions. The tested algorithms included, amongst others, SVMs and decision trees.…”

Section: Related Workmentioning

confidence: 99%

“…An autonomous system is commonly designed in a layered architecture: A perception layer perceives the environment through different sensors (1), the results are fused in a fusion layer (2). Based on this a situation interpretation is done (3).…”

Section: Introductionmentioning

confidence: 99%

“…This though somewhat differs from our task due to our focus on situation assessment without planning using supervised learning. More related to our field mostly either mathematical models [15], rule-based systems [9] or "classical" machine learning methods like Support Vector Machines (SVMs) or decision trees [8], [2] are used. In this paper, we propose the first supervised deep learning approach on non-visual data, namely a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

Scheel

Schwarz

Navab

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes. Such understanding is needed for planning of maneuvers, especially those that are particularly frequent such as lane changes. While in recent years advanced driver-assistance systems have made driving safer and more comfortable, these have mostly focused on car following scenarios, and less on maneuvers involving lane changes. In this work we propose a situation assessment algorithm for classifying driving situations with respect to their suitability for lane changing. For this, we propose a deep learning architecture based on a Bidirectional Recurrent Neural Network, which uses Long Short-Term Memory units, and integrates a prediction component in the form of the Intelligent Driver Model. We prove the feasibility of our algorithm on the publicly available NGSIM datasets, where we outperform existing methods.

show abstract

“…In [9], an optimal algorithm was developed for the coordination of CAVs' lane change behaviors at merging zones and the fuel consumption was evaluated through simulation. Recently, artificial intelligence based methodologies such as Neural Network [10], fuzzy logic [11] and decision tree [12] etc., have also attracted the attentions of researchers. In [13], comprehensive overviews of lane changing models were presented.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

Gong

Guo

Feng

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

The energy efficiency of Connected and Automated Vehicles (CAVs) is significantly influenced by surrounding road users. This paper presents the evaluation of energy efficiency of CAVs in a mixed traffic interacted with human controlled vehicles. To simulate the interaction between the CAVs and the cut-in vehicles controlled by human drivers near the intersection, a lane changing model is proposed to emulate the politeness and patience characteristics of the human driver.The proposed lane changing model is then calibrated based on over 100,000 naturalistic lane changing events collected by the University of Michigan Safety Pilot Model Deployment Program. A case study on simulation of the cut-in scenario is carried out to demonstrate the human driver's lane changing sensitivity under different driving trajectories of a frontal CAV and the influence on the energy consumption of the CAV due to the cut-in vehicle is evaluated. The simulation results indicate that the fuel economy of the CAV can be substantially improved if its surrounding cut-in vehicles can be well handled. (D. Zhao) understanding the factors which influence human drivers' lane change behaviors and how CAVs will interact with the human controlled vehicles plays an important role in the evaluation of energy efficiency of CAVs.

show abstract

Modeling tactical lane-change behavior for automated vehicles: A supervised machine learning approach

Cited by 32 publications

References 24 publications

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

Contact Info

Product

Resources

About