Integrated Strategy for Automatic Lane-Changing Decision and Trajectory Planning in Dynamic Scenario

2020 ◽  
Author(s):  
Meng Ren ◽  
Guangqiang Wu
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Driving Simulator ◽  
Back Propagation ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Lane Changing ◽  
Planning Strategy ◽  
Integrated Strategy ◽  
Dynamic Scenario

Abstract Automatic lane change is a necessary part for autonomous driving. This paper proposes an integrated strategy for automatic lane-changing decision and trajectory planning in dynamic scenario. The Back Propagation Neural Network (BPNN) is used in decision-making layer, whose prediction accuracy of the discretionary lane-changing is 94.22%. The planning layer determines the adjustable range of the average vehicle speed based on the size of the “lane-changing demand”, which is obtained based on the data of hidden layer in neural network, and then dynamically optimizes the lane-changing curve according to the vehicle speed and the current scenario. In order to verify the rationality of the proposed lane-changing architecture, simulation experiments based on a driving simulator is performed. The experiments show that the vehicle’s maximum lateral acceleration under the proposed lane-changing trajectory at a speed of 70km/h is about 0.1g, which means the vehicle has better comfort during lane-changing. At the same time, the proposed lane-changing trajectory is more in line with the human driver’s lane-changing trajectory compared with that of other planning strategy. Meanwhile, the planning strategy can also support the lane-changing trajectory planning on a curved road.

scholarly journals A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chenxi Ding ◽  
Wuhong Wang ◽  
Xiao Wang ◽  
Martin Baumann
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Driving Simulator ◽  
Back Propagation ◽  
Urban Traffic ◽  
Lane Changing ◽  
Training Time

The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Experimental Data

2006 ◽  
Author(s):  
Stefano Melzi ◽  
Edoardo Sabbioni ◽  
Alessandro Concas ◽  
Marco Pesce
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Lateral Acceleration ◽  
Slip Angle ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Training Stage ◽  
Wide Range ◽  
Self Adaptation

This work explores the possibility of using a non-structured algorithm as a sideslip angle valuer: on the basis of a preliminary numerical analysis, a neural network was designed and trained with experimental signals of lateral acceleration, vehicle speed, yaw rate and steer angle. The network was applied to experimental data in order to verify its capability of self-adaptation to changes in friction coefficient and to provide accurate estimations for manoeuvres sensibly different from the ones used during the training stage. The simple architecture joined with an appropriate training set conferred good self-adaptation properties to the neural network which was able to provide satisfying estimation of side slip angle for a wide range of manoeuvres and different friction conditions.

Trajectory Planning Strategy for the Links of a Walking Human-Machine System Using a Neural Network

2021 ◽  
pp. 255-261
Author(s):  
Sergey F. Jatsun ◽  
Andrei V. Malchikov ◽  
Alexey A. Postolniy ◽  
Andrey S. Yatsun
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Planning Strategy ◽  
Machine System

A dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles

2021 ◽  
pp. 095440702098271
Author(s):  
Xiao Liu ◽  
Jun Liang ◽  
Junwei Fu
Keyword(s):  
Trajectory Planning ◽  
Planning Method ◽  
Reference Trajectory ◽  
Automated Vehicles ◽  
Lane Changing ◽  
Planning Strategy ◽  
V2v Communication ◽  
Starting Point ◽  
Trajectory Tracking Controller ◽  
Dynamic Trajectory

This paper describes a dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles (CAVs). The proposed dynamic lane-changing trajectory planning (DLTP) model adopts vehicle-to-vehicle (V2V) communication to generate an automated lane-changing maneuver with avoiding potential collisions and rollovers during the lane-changing process. The novelty of this method is that the DLTP model combines a detailed velocity planning strategy and considers more complete driving environment information. Besides, a lane-changing safety monitoring algorithm and a lane-changing starting-point determination algorithm are presented to guarantee the lane-changing safety, efficiency and stability of automated vehicles. Moreover, a trajectory-tracking controller based on model predictive control (MPC) is introduced to make the automated vehicle travel along the reference trajectory. The field traffic data from NGSIM are selected as the target dataset to simulate a real-world lane-changing driving environment. The simulations are performed in CarSim-Simulink platform and the experimental results show that the proposed method is effective for lane-changing maneuver.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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