Dynamic motion planner with trajectory optimisation for automated highway lane‐changing driving

Abstract

This study proposes a dynamic motion planner with trajectory optimisation for automated highway lane-changing driving. Owing to the connected and automated vehicles (CAVs) technology that the real-time traffic information can be obtained, alternative trajectories can be generated to satisfy the vehicle kinematic constraints and avoid many types of potential collisions. An optimal control theory is adopted to select an optimal lane-changing path from the finite path set, and the appropriate acceleration and speed for the execution path are also determined. In order to avoid unnecessary motion replanning process, this study puts forward a collision-avoidance monitoring algorithm to reduce the time consumption costs of the motion planner. Moreover, an online planning framework based on 'decision-execution' is explored. Applying this timeline framework can not only help to evaluate the dynamic planner's online performance, but also reduce the deviation between the online calculation and the actual execution caused by the time consumption. The simulations are performed in PreScan-Simulink platform and the experimental results show that the presented dynamic planner can complete the lane-changing manoeuvre safely and effectively in a high-speed environment.

Dynamic driving intention recognition of vehicles with different driving styles of surrounding vehicles

“…Extracting complete lane changing and lane keeping samples from datasets is the basis of identifying driving intention accurately. In this section, taking the extraction of complete left and right lane changing and lane keeping data samples from NGSIM-I80 dataset as an example, the steps of data preprocessing are described as follows [33,34]:…”

“…Extracting complete lane changing and lane keeping samples from datasets is the basis of identifying driving intention accurately. In this section, taking the extraction of complete left and right lane changing and lane keeping data samples from NGSIM‐I80 dataset as an example, the steps of data preprocessing are described as follows [33, 34]: Step 1. Select the vehicle trajectory data with vehicle type of car in I‐80 original dataset, and eliminate the vehicle trajectory data with vehicle type of motorcycle and truck. Step 2.…”

A Review of the Motion Planning and Control Methods for Automated Vehicles

“…The global path planning module generates a macroscopic path based on the start and end points, but it does not take into account the real-time environment of certain vehicles. The local route planning module of the automated vehicle generates a route with traffic and avoidance functions based on real-time environment information for the control module to follow [7][8][9].…”

Spatio‐temporal decision‐making and trajectory planning framework with flexible constraints in closed‐loop dynamic traffic

“…In the backward direction, the strategy and planning results of the ego vehicle also influence the future trajectory of other vehicles, causing the prediction results to vary. Therefore, the models of prediction like [3][4][5], the design of decision-making algorithms [6,7] and the development of planning schemes [8][9][10] should be fully incorporated to realize safe navigation in a dynamic environment.…”