On the Application of Machine Learning for Cut-in Maneuver Recognition in Platooning Scenarios

Bouhoute, Afaf; Mosbah, Mohamed; Zemmari, Akka; Berrada, Ismaïl

doi:10.1109/vtc2020-spring48590.2020.9129443

Cited by 6 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with previous research [27][28][29][30][31][32][33], the main advantages of this research are reflected in the following aspects. A large amount of naturalistic driving data was collected in this research, and a STACKING-a was proposed for the recognition of LCM.…”

Section: Discussionmentioning

confidence: 95%

See 1 more Smart Citation

Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway

Zhang

Guo

Wang

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

The recognition of the preceding vehicle lane‐changing manoeuvre (LCM) is essential for improving the rationality and safety of the decision‐making of driverless vehicles. However, traditional recognition researches for preceding vehicle LCM are generally characterized by problems such as low recognition accuracy and poor research foundation. In response to these problems, this paper carried out naturalistic driving study (NDS) on the highway, from which a large amount of on‐road data consisting of lane‐keeping (LK) and lane‐changing left and right (LCL, LCR) manoeuvres were collected. A stacking‐based ensemble learning method for the recognition of the LCM of the preceding vehicle, which integrates the random forest, support vector machine, long and short‐term memory network (LSTM), and bi‐directional LSTM based on attention mechanism (AT‐Bi‐LSTM) algorithms, is proposed. Compared with traditional machine learning methods, the proposed method exhibits greater advantages in terms of its recognition accuracy. It is particularly of note that the recognition accuracy of the model at 0.4 s and 0.8 s after the LCM reached 90.77% and 95.54%, respectively. The study reported is of great significance for the construction of more intelligent vehicle‐vehicle collaboration and the promotion of the industrial applications of intelligent vehicle technology.

show abstract

Section: Discussionmentioning

confidence: 95%

“…Compared with previous research [27–33], the main advantages of this research are reflected in the following aspects. (1) Numerous naturalistic driving experiments were carried out, and the proposed model is more comprehensive, reliable, and realistic than existing models.…”

Section: Discussionmentioning

confidence: 97%

Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway

Zhang

Guo

Wang

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Regarding to RQ3, we distinguished four types of validation strategies as illustrated in Section 5. We remark that a vast amount of works [24,27,31,59] have adopted simulation to imitate the behaviour of vehicles and evaluate a set of performance criteria. Despite experimentation can show solutions efficiency in a real world, it suffers from the expensive technologies requirement.…”

Section: Discussionmentioning

confidence: 99%

Vehicle Platooning Systems: Review, Classification and Validation Strategies

Fakhfakh¹,

Tounsi²,

Mosbah³

2020

IJNDC

Self Cite

View full text Add to dashboard Cite

The increase in vehicle numbers has resulted in the growth of traffic jams in cities and highways, thereby raising various issues on fuel consumption, environmental pollution, and traffic safety [1]. Platooning is an Intelligent Transport System (ITS) [2] application which has emerged as a promising solution for the traffic management in highways. The main idea of vehicle platooning suggests that a set of vehicles travel together while maintaining a small distance between each other. This can lead to an increase in traffic capacity and then an improved traffic management and a reduced travel time. Moreover, the comfort and the safety of passengers are enhanced since the scenarios of extreme acceleration or deceleration are eliminated and the platoon vehicles are considered as a single unit. Furthermore, the emission performance and the fuel economy are significantly improved. A vehicle platoon (also called "convoy") can be seen as a group of vehicles that travel in close coordination through a headway control mechanism. These vehicles maintain a short spacing between them and a relative velocity. The vehicle in the front position, called leader, represents the trajectory and velocity reference. It controls all the following vehicles in the platoon. Each vehicle of the platoon receives orders from the leader that may be communicated either directly or by the preceding vehicle.As platooning system is a critical system, safety is an important issue. However, safety is exposed to several challenges if the system does not achieve its goal without any disturbance. For instance, it is important to ensure that vehicles do not get too close and react within a certain time frame during emergencies. Then, platooning systems need to be validated to ensure a reliable behaviour. For that, it is necessary to clearly define validation strategies (formal verification, simulation, etc.) as this has already been used in critical fields like healthcare [3] and avionics [4]. That is why, we make emphasis in this paper on the works that deal with validation methods and techniques for platooning systems. Related Surveys and Scope of the PaperThere are several useful surveys which have been conducted to deal with some aspects related to platooning algorithms.In Kavathekar and Chen [5], the authors introduce several existing algorithms which focus on vehicle platooning. Besides, they detail some methodologies for obstacle detection and collision avoidance. Furthermore, they are interested in inter-vehicle communication techniques which allow vehicles to share some information such as the velocity, acceleration and detected obstacles. In the same context, the survey of Jia et al.[6] provides a valuable insight about platoon-based vehicular systems. The main issues related to these systems are analysed such as the platoon management and the communication inter-and intra-platoon.The study of Kulla et al. [7] presents a survey in which they introduce a classification of vehicular communication methods which aim to control vehicles. Moreover...

show abstract

“…They found that as the complexity of the problem increases, the performance of the controller deteriorates. Among the methods that potentially approach optimality, artificial intelligence-based algorithms have undergone considerable development in recent years [11,12]. As an example, in [11], different supervised learning algorithms used for the identification of cut-in maneuvers are compared and optimal performances are found in the Gradient Boosting algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Among the methods that potentially approach optimality, artificial intelligence-based algorithms have undergone considerable development in recent years [11,12]. As an example, in [11], different supervised learning algorithms used for the identification of cut-in maneuvers are compared and optimal performances are found in the Gradient Boosting algorithm. However, their robustness and real-time implementation are still open points due to their computational burden.…”

Section: Introductionmentioning

confidence: 99%

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

et al. 2021

View full text Add to dashboard Cite

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred.

show abstract

On the Application of Machine Learning for Cut-in Maneuver Recognition in Platooning Scenarios

Cited by 6 publications

References 14 publications

Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway

Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway

Vehicle Platooning Systems: Review, Classification and Validation Strategies

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

Contact Info

Product

Resources

About