Enhancing Passenger Comfort in Autonomous Vehicles Through Vehicle Handling Analysis and Optimization

Mohajer, Navid; Nahavandi, Saeid; Asayesh, Hamid; Najdovski, Zoran

doi:10.1109/mits.2019.2953533

Cited by 29 publications

(13 citation statements)

References 33 publications

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“…Similar to the kinematic model, a dynamic bicycle model is generally used for designing PTCs [6,[11][12][13][14]. Figure 2 shows the geometry of a dynamic bicycle model.…”

Section: Dynamic Modelmentioning

confidence: 99%

“…PTC of the AV is an interface between vehicle dynamic and path planner of the AV. Since this unit has access to the ride and handling control subsystems of the vehicle, for example steering and lateral control, it can be used to enhance the dynamic behaviour of the vehicle while implementing an accurate path tracking task [6]. PTC of the AV normally have access to the sensory feedbacks, for example IMU‐GPS information, and use them in order to constitute a closed‐loop control scheme.…”

Section: Introductionmentioning

confidence: 99%

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Review and performance evaluation of path tracking controllers of autonomous vehicles

Rokonuzzaman

Mohajer

Nahavandi

et al. 2021

IET Intelligent Trans Sys

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Autonomous Vehicles (AVs) have shown indelible and revolutionary effects on accident reduction and more efficient use of travel time, with outstanding socio‐economic impact. Despite these benefits, to make AVs accepted by a wide demographic and produce them on an industrial scale with a reasonable price, there are still a number of technological and social challenges that need to be tackled. Path Tracking Controller (PTC) of AVs is one of the high potential subsystems that can be further improved in order to achieve more accurate, robust and comfortable tracking performance. This study provides a critical review and simulation study of several selected techniques used for the design of PTC of AVs. The AVs are assumed to have limited controllability due to non‐holonomic constraints, such as car‐like vehicles and differential drive mobile robots. A detailed discussion will be provided on the simulation outcomes as well as the pros and cons of each technique for the sake of implementation and improvement of state‐of‐the‐art PTC.

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“…Similar to the kinematic model, a dynamic bicycle model is generally used for designing PTCs [6,[11][12][13][14]. Figure 2 shows the geometry of a dynamic bicycle model.…”

Section: Dynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review and performance evaluation of path tracking controllers of autonomous vehicles

Rokonuzzaman

Mohajer

Nahavandi

et al. 2021

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

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“…Vehicle comfort has been evaluated in terms of handling behaviour via a cost function first introduced in [7], where the cost function is a combination of Low-frequency Highmagnitude Horizontal, Yaw and Roll motions. The objective functions have been summarised below:…”

Section: B Cost Functionmentioning

confidence: 99%

“…An electric wheelchair was used as the platform and the objective result was a path with a better view of the other sides of turns to be more comfortable. Human comfort in terms of enhancing the controller was discussed and optimised in [7]. A cost function was developed based on five factors defined using techniques like root mean square (RMS) and fast Fourier transforms (FFT), the cost function was then used to optimise the controller to follow the paths defined using Clothoids in the most comfortable manner.…”

Section: Introductionmentioning

confidence: 99%

Ergonomic Path Planning for Autonomous Vehicles - An investigation on the effect of transition curves on Handling Behaviour

Siddiqi¹,

Saharkhiz²,

Jazar³

et al. 2020

2020 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE)

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“…MPC has shown to be a suitable option for AVs that can accommodate controller design with a reasonable level of complexity and computational cost. In addition, this control approach has the potential to ensure and increase the comfort of the AVs' passengers by improving handling performance of an AV [17]. One of the major advantages of MPC is its ability to handle multiple variables and constraints.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

et al. 2021

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Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost for MPC. A data-driven approach realised by learning vehicle dynamics using vehicle operation data can offer a promising solution by providing a suitable trade-off between accurate state predictions and the computational cost for MPC. This work proposes a framework for designing an MPC with a Neural Network (NN)-based learned dynamic model of the vehicle using the plethora of data available from modern vehicle systems. The objective is to integrate an NN-based model with higher accuracy than the conventional vehicle models for the required prediction horizon into MPC for improved tracking performances. The proposed NN-based model is highly capable of approximating latent system states, which are difficult to estimate, and provides more accurate predictions in the presence of parametric uncertainties. The observation of the results in various road conditions shows that the proposed approach outperforms the MPCs with conventional vehicle models.

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Enhancing Passenger Comfort in Autonomous Vehicles Through Vehicle Handling Analysis and Optimization

Cited by 29 publications

References 33 publications

Review and performance evaluation of path tracking controllers of autonomous vehicles

Review and performance evaluation of path tracking controllers of autonomous vehicles

Ergonomic Path Planning for Autonomous Vehicles - An investigation on the effect of transition curves on Handling Behaviour

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

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