Velocity Control Strategies to Improve Automated Vehicle Driving Comfort

Du, Yuchuan; Liu, Chenglong; Li, Yishun

doi:10.1109/mits.2017.2776148

Cited by 81 publications

(49 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the speed control strategy can effectively improve the comfort. Du et al [59] proposed a disturbance rate model to describe the individual vibration sensitivity. The theoretical speed was calculated to maximize passenger comfort, and the annoyance rate was used to modify the evaluation results.…”

Section: Anthropomorphic Decision Makingmentioning

confidence: 99%

A review on key challenges in intelligent vehicles: Safety and driver‐oriented features

Zhou

Zhang

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

The huge advantages of intelligent vehicles (IVs) in improving road safety and operating efficiency have become a research focus in the industry. IVs have made significant progress in recent years, but it is still face great challenges in order to be accepted by users on a large scale. In this regard, the authors propose that the research of IVs can be developed along the lines of safety, comfort and economy, gradually overcoming existing dilemmas. First of all, security is the most basic requirement of IVs. The authors sort out the key technologies and challenges of the basic architecture of IVs, and propose existing attack and defences strategies for IVs information security technology. Secondly, comfort is more about people's subjective feelings. From two aspects of physiological comfort and psychological comfort, the paper studies the anthropomorphic decision-making to overcome the mechanized speed control, human computer interaction design, personalized driving style and ethical decision-making methods. Then, aiming at the micro-and macro-levels of economy, it outlines technologies such as economic driving behavioural, collaborative control of people, vehicles and roads and IVs sharing. Finally, the authors summarized the challenges and future development directions in the three stages of IVs development.

show abstract

Section: Anthropomorphic Decision Makingmentioning

confidence: 99%

A review on key challenges in intelligent vehicles: Safety and driver‐oriented features

Zhou

Zhang

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Besides all novel technical methodologies and improvements in ADAS and AV, less attention is paid to human comfort, that will be influenced by AVs [4]. When looking at the number of publications in the automotive domain, the last decade shows an increase, but at the same time the study of the overall comfort of a humans during a vehicle trip, also defined as ride comfort, is a relatively new research field [1], [5]. When vibrations in a vehicle exceed certain thresholds, the occupants feel a discomfort and the ride quality decreases.…”

Section: Background and Motivationmentioning

confidence: 99%

Identification of critical ride comfort sections by use of a validated vehicle model and Monte Carlo simulations

Genser

Nitsche

Kouvelas

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

A coherent way to enhance the user acceptance of autonomous vehicles (AV) is to ensure maximum ride comfort along the driven route. This paper proposes a sub-microscopic simulation framework that can be utilized to assess the ride comfort based on data from vehicle dynamics. In a future connected vehicle environment, this work can be used to enable an optimized route and motion planning, by avoiding sections with poor ride comfort and/or adapting the driving style and behavior. The developed methodology proposes a process chain for producing accurate and representative comfort estimates, by utilizing a road surface model, a non-linear model optimization, and Monte Carlo simulations. A case study with three real road sites demonstrates the effective tuning of the framework with real data and achieves high-resolution comfort results. The simulation investigations of the developed framework provide results and insights that justify the importance of enhancing available data sources with ride comfort data.

show abstract

“…Definition 1 [36]: Given a scalar γ > 0 and a matrix Q > 0, if the inequality (19) holds for k ∈ [0, N ], then the time varying EES (17) satisfies the FH H ∞ performance. 2 + e(0) T Qe(0) . ( 19)…”

Section: Non-fragile Estimator and Eesmentioning

confidence: 99%

“…Vehicle intelligence is a main trend in both automotive and transportation fields, and the research of intelligent vehicle mainly focuses on improving the safety, as well as providing excellent human-vehicle interface, and so on [1], [2]. This field attracts a lot of attention from researchers, and many theories have been put forward.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking With Constrained Sensors and Unreliable Communication Networks

Liu

Rao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This work investigates the remote vehicle tracking issue over constrained monitoring sensors and unreliable communication networks. A saturation function is used to describe the bounded time varying acceleration of the vehicle. A set of matrices are introduced to model the sensor monitoring conditions called captured states (CSs), and a Markov chain with time varying and partially unknown transition probability (TVPUTP) is proposed to analyze the conditions of the CSs. Then, a CS dependent nonfragile estimator is designed based on the measured unreliable vehicle information, and the estimation error system (EES) is derived. Two theorems are established to ensure that the EES satisfies the finite-horizon (FH) H ∞ performance. Finally, an example is introduced to show the effectiveness of the results.

show abstract

Velocity Control Strategies to Improve Automated Vehicle Driving Comfort

Cited by 81 publications

References 20 publications

A review on key challenges in intelligent vehicles: Safety and driver‐oriented features

A review on key challenges in intelligent vehicles: Safety and driver‐oriented features

Identification of critical ride comfort sections by use of a validated vehicle model and Monte Carlo simulations

Trajectory Tracking With Constrained Sensors and Unreliable Communication Networks

Contact Info

Product

Resources

About