Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

Jiang, Haobin; Shi, Kaijin; Cai, Junyu; Chen, Long

doi:10.1049/iet-its.2018.5224

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…In addition, the maneuver duration could be simply defined as the length of the path divided by the velocity. 10,14,17 Therefore, another cost function to take minimizing the duration into consideration 16 will be like so:…”

Section: Trajectory Generatingmentioning

confidence: 99%

A real-time optimal cooperative lane change strategy via V2V communication

Maleki

Taghavipour

Azadi

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Safety, which is in danger by careless maneuvers, is the most significant aspect of driving due to the health of passengers. The lane-changing procedure (LCP) could result in an accident provided that the driver miscalculates other vehicle positions and velocities. Consequently, automation and vehicle-to-vehicle (V2V) communication, which is a way of exchanging information between cars, have become increasingly popular owing to improving safety. Besides, real-time implementation of a fast strategy dealing with such an amount of information seems to be essential in a high-speed maneuver. This article presents a novel approach to complete the process considering optimality and safety without a huge volume of computation. This means that a constrained-optimization problem (COP) is examined to find datasets and discover simpler correlations that are effortless to implement in the real world. Ultimately, the dataset is incorporated with a model predictive controller and the effectiveness of the strategy is validated in the Siemens PreScan® platform. Moreover, the results show that the presented method is effective for a simple, safe, comfortable, and efficient lane-changing maneuver.

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Section: Trajectory Generatingmentioning

confidence: 99%

A real-time optimal cooperative lane change strategy via V2V communication

Maleki

Taghavipour

Azadi

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…In order to compare the differences between the traditional preview-based driver models and human drivers on the same road, three preview-based driver models (single-point preview, two-point preview, and multi-point preview driver models) are simulated in PreScan+Simulink, respectively. PreScan is an active experimental platform [38], and it is able to build scenes based on the actual road environment and sensor model.…”

Section: Simulationmentioning

confidence: 99%

Implementation of Human-Like Driver Model Based on Recurrent Neural Networks

Jiang

Zhou

et al. 2019

IEEE Access

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Driver model is the most basic and important model for moving direction control of autonomous vehicles and it has been extensively studied from the perspective of precision and robustness of control, driving safety, and riding comfort. However, human-like driver model is a rarely mentioned research issue. In this paper, we first establish three preview-based driver models in PreScan+Simulink and collect human drivers' steering data on four two-way and two-lane free curved roads with 20 experienced drivers and one experimental vehicle under four specified speeds. Then, the similarities between steering wheel angles of preview-based models and those of human drivers are compared through dynamic time warping (DTW). From the calculation results of DTW and analysis of human drivers' gaze positions, it shows that the preview-based models are hard to reflect the characteristics of human drivers' maneuver. To this end, we propose a human-like driver model based on the continuity of human drivers' steering wheel angles. The experienced drivers' steering wheel angels are modeled with three different kinds of multivariate multi-step recurrent neural networks (RNNs) and the inputs of models are historical speeds, historical road curvatures, future road curvatures, and historical steering wheel angles, as well as the outputs are future steering wheel angles. By comparing the three RNN-based driver models with different configuration structures and historical steps, it is found that the long short-term memory (LSTM) model has the best prediction performance in validation and testing period. In this way, a data-driven human-like driver model is developed to generate human-like steering wheel angles on curved roads.

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“…In addition, Yang et al’s model took into account the situation of collision-avoidance and rollover-avoidance, but the model only paid attention to vehicles on the target lane, neglecting the current lane vehicles’ influence on the safety of lane-changing maneuver. Jiang et al 26 focused on the trajectory planning method of intelligent vehicle lane changing emergently. The model divides the emergency lane-changing process into the initial stage and tracking stage for trajectory planning based on road steering experiment and sigmoid functions.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Liang

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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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.

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Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

Cited by 15 publications

References 27 publications

A real-time optimal cooperative lane change strategy via V2V communication

A real-time optimal cooperative lane change strategy via V2V communication

Implementation of Human-Like Driver Model Based on Recurrent Neural Networks

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

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