Research of basic issues of autonomous mobility

Bartuška, Ladislav; Łabudzki, Remigiusz

doi:10.1016/j.trpro.2020.02.031

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Taking into account the area of social benefits, it should be emphasized that autonomous driving does not lead to a loss of safety or efficiency of road transport, but rather improves them [20]. It may also result in new business models, as driverless cars will enable car sharing at much higher levels than is currently the case; in the vast majority of cases, it will be cheaper to use these shared cars than your own, without the financial burden and hassle of owning a private car (frozen capital, taxes, insurance, repairs and parking difficulties) [21,22]. The appropriate and adequate information about the number of free places, their location, etc., can reduce even by 30% the traffic volume in some cases [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

et al. 2021

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New technologies reaching out for meeting the needs of an aging population in developed countries have given rise to the development and gradual implementation of the concept of an autonomous vehicle (AV) and have even made it a necessity and an important business paradigm. However, in parallel, there is a discussion about consumer preferences and the willingness to pay for new car technologies and intelligent vehicle options. The main aim of the study was to analyze the impact of selected factors on the perception of the future of autonomous cars by respondents from the area of Southeastern Poland in terms of a comparison with traditional cars, with particular emphasis on the advantages and disadvantages of this concept. The research presented in this study was conducted in 2019 among a group of 579 respondents. Data analysis made it possible to identify potential advantages and disadvantages of the concept of introducing autonomous cars. A positive result of the survey is that 68% of respondents stated that AV will be gradually introduced to our market, which confirms the high acceptance of this technology by Poles. The obtained research results may be valuable information for governmental and local authorities, but also for car manufacturers and their future users. It is an important issue in the area of shaping the strategy of actions concerning further directions of development on the automotive market.

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Section: Literature Reviewmentioning

confidence: 99%

Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

et al. 2021

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“…With the rapid development of science and technology, the automobile industry has become one of the most important industries in the world, playing an essential role in daily life, industrial production, and other fields [ 1 , 2 , 3 ]. In recent years, autonomous vehicles that can be characterized as driving that do not need a driver but are only navigated by sensors, detection, and computer systems are getting more and more attention and are supposed to bring many benefits to society [ 4 , 5 ]. However, as it remains challenging to make self-driving cars absolutely safe, the public is skeptical that they can be put to mass use anytime soon [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Localization and Mapping Algorithm Based on Improved LVI-SAM for Vehicles in Field Environments

Han

Shi

Wang

2023

Sensors

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Quickly grasping the surrounding environment’s information and the location of the vehicle is the key to achieving automatic driving. However, accurate and robust localization and mapping are still challenging for field vehicles and robots due to the characteristics of emptiness, terrain changeability, and Global Navigation Satellite System (GNSS)-denied in complex field environments. In this study, an LVI-SAM-based lidar, inertial, and visual fusion using simultaneous localization and mapping (SLAM) algorithm was proposed to solve the problem of localization and mapping for vehicles in such open, bumpy, and Global Positioning System (GPS)-denied field environments. In this method, a joint lidar front end of pose estimation and correction was designed using the Super4PCS, Iterative Closest Point (ICP), and Normal Distributions Transform (NDT) algorithms and their variants. The algorithm can balance localization accuracy and real-time performance by carrying out lower-frequency pose correction based on higher-frequency pose estimation. Experimental results from the complex field environment show that, compared with LVI-SAM, the proposed method can reduce the translational error of localization by about 4.7% and create a three-dimensional point cloud map of the environment in real time, realizing the high-precision and high-robustness localization and mapping of the vehicle in complex field environments.

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“…The advent of autonomous vehicles has the potential to drastically change society and the way we understand, plan and design cities and regions [7][8][9]. It will make transportation smoother, but the question is whether it will make it safer [10,11]. For preview control of autonomous vehicles in terms of accuracy and safety, it is critical to investigate the coupling between lateral and longitudinal dynamics for accurate data acquisition, which is necessary for deep learning modelling [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving

Nie

Min

Pan

et al. 2022

Sensors

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Multibody models built in commercial software packages, e.g., ADAMS, can be used for accurate vehicle dynamics, but computational efficiency and numerical stability are very challenging in complex driving environments. These issues can be addressed by using data-driven models, owing to their robust generalization and computational speed. In this study, we develop a deep neural network (DNN) based model to predict longitudinal-lateral dynamics of an autonomous vehicle. Dynamic simulations of the autonomous vehicle are performed based on a semirecursive multibody method for data acquisition. The data are used to train and test the DNN model. The DNN inputs include the torque applied on wheels and the vehicle’s initial speed that imitates a double lane change maneuver. The DNN outputs include the longitudinal driving distance, the lateral driving distance, the final longitudinal velocities, the final lateral velocities, and the yaw angle. The predicted vehicle states based on the DNN model are compared with the multibody model results. The accuracy of the DNN model is investigated in detail in terms of error functions. The DNN model is verified within the framework of a commercial software package CarSim. The results demonstrate that the DNN model predicts accurate vehicle states in real time. It can be used for real-time simulation and preview control in autonomous vehicles for enhanced transportation safety.

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Research of basic issues of autonomous mobility

Cited by 17 publications

References 7 publications

Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

A Localization and Mapping Algorithm Based on Improved LVI-SAM for Vehicles in Field Environments

Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving

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