Lane level turning trajectory tracking of intelligent vehicle based on drivers' manipulate habits

Wang, Shufeng; Zhang, Da‐Wei; Junhui, An; Zhang, Junyou

doi:10.1109/ccdc.2017.7978418

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…Currently, the study of Microscopic vehicle trajectory prediction mainly focuses on automatic driving vehicle control [18], collision detection [19], traffic data mining [8] [20] [21], and vehicle network dynamic planning [22]. Vehicle trajectory prediction methods can be roughly divided into two categories, physical/maneuver-based models and interaction awarenessbased models.…”

Section: B Vehicle Trajectory Predictionmentioning

confidence: 99%

“…In contrast, the maneuver-based prediction method needs to identify the vehicle operation first, and then uses the identified operation to predict the future trajectory of the vehicle. [8] proposes a vehicle turning trajectory tracking control algorithm based on vehicle dynamics constraints, which can make the trajectory tracking of intelligent vehicles meet the requirements of urban road level accuracy. Schreier et al [23] apply the Bayesian inference network to estimate the current driving action of each vehicle.…”

Section: B Vehicle Trajectory Predictionmentioning

confidence: 99%

“…As a solution, traffic prediction is a key component to realize ITS and vehicular networks by expecting the foreseen future on the road. The traffic prediction can be divided into two categories, macroscopic traffic flow prediction [3], [4], [5] and microscopic vehicle trajectory prediction [6], [7], [8]. The macroscopic traffic flow reflects the vehicle flow, speed, and density of each road section.…”

mentioning

confidence: 99%

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A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Zhao

Liu

Al‐Dubai

et al. 2021

IEEE Internet Things J.

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Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by processing and propagation of the routing request and response. Thus, to predict the highprecision trajectory beyond the state-of-the-art, we propose a Generative Adversarial Network-based VEhicle trajEctory Prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components, 1) vehicle coordinate transformation for data set preparation, 2) neural network prediction model trained by GAN, and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver's psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the Average Accuracy, Mean Absolute Error, and Root Mean Squared Error.

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Section: B Vehicle Trajectory Predictionmentioning

confidence: 99%

Section: B Vehicle Trajectory Predictionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Zhao

Liu

Al‐Dubai

et al. 2021

IEEE Internet Things J.

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“…In view of navigation of CV, Kawatsu showed that stereo vision-based navigation was a promising and affordable mechanism [16]. On the basis of drivers' manipulative habits, the turning trajectory tracking control algorithm of lane levels was proposed in accordance with GIS and GPS information [17]. Cui et al proposed an optimal control method to achieve the path tracking mission for a visionbased intelligent vehicle [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Distribution Framework and Algorithms for Connected Logistics Vehicles

Wang

Yong

et al. 2020

IEEE Access

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This study presents an intelligent distribution framework based on edge computing and proposes navigation and obstacle avoidance algorithms for connected logistics vehicles (CLVs) on the basis of Trimble BD982 positioning sensor and tentacle algorithm (TA). An edge computing framework for the distribution of CLVs is established, and the functions of three layers (cloud server, edge equipment, and terminal) are described in detail. The basic functions, hardware, and software systems of the CLV are designed and presented. Focusing on autonomous driving, a Global Positioning System (GPS) navigation algorithm and an obstacle avoidance control strategy on the layer of edge equipment are developed on the basis of the TA. Autonomous GPS navigation is realized by combining the entire road network with the local road network to avoid obstacles. The TA is improved to help the CLV for avoiding obstacles. Experiments show that the hardware system and designed algorithms of the CLV are effective. The tracking error on the straight-line track is within 3 cm, the change rate of longitudinal velocity is within 0.3 g/s, the change rate of tire side deflection angle is less than 1°/s, and the calculation time is shortened by 25% when the calculation time is 30 ms. These results indicate that the vehicle has good stability and performance during obstacle avoidance in real time, and the proposed algorithms are superior to traditional algorithms. The CLV can realize autonomous GPS navigation, with high navigation accuracy, reliable obstacle avoidance performance, and stable vehicle handling.

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“…Intelligent transportation systems (ITS) manage traffic by using new services for various transport modes [23]. The objective of ITS is to provide an improved system by informing users about traffic situations and by making mobility coordination safer and smarter [24]. In recent years, ITS has been widely applied along with the development of IT technologies such as robotics, signal and image processing, computing, sensing, and communications [25].…”

Section: B Intelligent Transportation Systems (Its) and Autonomous Intersection Management (Aim)mentioning

confidence: 99%

Intelligent Intersection Management Systems Considering Autonomous Vehicles: A Systematic Literature Review

Namazi

2019

IEEE Access

120

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Over the past several decades, the development of technologies and the production of autonomous vehicles have enhanced the need for intelligent intersection management systems. Subsequently, growing interest in studying the traffic management of autonomous vehicles at intersections has been evident, which indicates a critical need to conduct a systematic literature review on this topic. This paper offers a systematic review of the proposed methodologies for intelligent intersection management systems and presents the remaining research gaps and possible future research approaches. We consider both pure autonomous vehicle traffic and mixed traffic at four-way signalized and unsignalized intersection(s). We searched for articles published from 2008 to 2019, and identified 105 primary studies. We applied the thematic analysis method to analyze the extracted data, which led to the identification of four main classes of methodologies, namely rule-based, optimization, hybrid, and machine learning methods. We also compared how well the methods satisfy their goals, namely efficiency, safety, ecology, and passenger comfort. This analysis allowed us to determine the primary challenges of the presented methodologies and propose new approaches in this area.INDEX TERMS Autonomous vehicle, intelligent intersection management system, mixed traffic, vehicleto-infrastructure (V2I) communication, vehicle-to-vehicle (V2V) communication.

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Lane level turning trajectory tracking of intelligent vehicle based on drivers' manipulate habits

Cited by 4 publications

References 4 publications

A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Intelligent Distribution Framework and Algorithms for Connected Logistics Vehicles

Intelligent Intersection Management Systems Considering Autonomous Vehicles: A Systematic Literature Review

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