scholarly journals A Universal Trajectory Planning Method for Automated Lane-Changing and Overtaking Maneuvers

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Ying Wang ◽  
Chong Wei
Keyword(s):  
Trajectory Planning ◽  
Mathematical Optimization ◽  
Initial Position ◽  
Planning Method ◽  
Reference Trajectory ◽  
Time Intervals ◽  
Simulation Experiments ◽  
Lane Changing ◽  
Traffic State ◽  
Mathematical Optimization Model

Lane-changing and overtaking are conventional maneuvers on roads, and the reference trajectory is one of the prerequisites to execute these maneuvers. This study proposes a universal trajectory planning method for automated lane-changing and overtaking maneuvers, in which the trajectory is regarded as the combination of a path and its traffic state profiles. The two-dimensional path is represented by a suitable curve to connect the initial position with final position of the ego vehicle. Based on the planned path, its traffic state profiles are generated by solving a nonlinear mathematical optimization model. Moreover, the study discretizes the time horizon into several time intervals and determines the parameters to obtain the continuous and smooth profiles, which guarantees the safety and comfort of the ego vehicle. Finally, a series of simulation experiments are performed in the MATLAB platform and the results show the feasibility and effectiveness of the proposed universal trajectory planning method.

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.

scholarly journals Lane‐changing trajectory planning method for automated vehicles under various road line‐types

2020 ◽  
Vol 2 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Ying Wang ◽  
Chong Wei ◽  
Erjian Liu ◽  
Shurong Li
Keyword(s):  
Trajectory Planning ◽  
Planning Method ◽  
Automated Vehicles ◽  
Lane Changing

scholarly journals Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing
Keyword(s):  
Fuel Consumption ◽  
Trajectory Planning ◽  
Signalized Intersections ◽  
Planning Method ◽  
Signalized Intersection ◽  
Driver Model ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Traffic Lights ◽  
Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

Trajectory planning for coordinately operating robots

1990 ◽  
Vol 4 (3) ◽  
pp. 165-177 ◽  
Author(s):  
Y. P. Chien ◽  
Qing Xue
Keyword(s):  
Trajectory Planning ◽  
Three Dimensional ◽  
Initial Position ◽  
Minimum Time ◽  
Future Research ◽  
Rigid Object ◽  
Redundant Robots ◽  
Planning Algorithm ◽  
The Common ◽  
Joint Velocity

An efficient locally minimum-time trajectory planning algorithm for coordinately operating multiple robots is introduced. The task of the robots is to carry a common rigid object from an initial position to a final position along a given path in three-dimensional workspace in minimum time. The number of robots in the system is arbitrary. In the proposed algorithm, the desired motion of the common object carried by the robots is used as the key to planning of the trajectories of all the non-redundant robots involved. The search method is used in the trajectory planning. The planned robot trajectories satisfy the joint velocity, acceleration and torque constraints as well as the path constraints. The other constraints such as collision-free constraints, can be easily incorporated into the trajectory planning in future research.

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