Smooth longitudinal driving strategy with adjustable nonlinear reference model for autonomous vehicles

Mohtavipour, Seyed Mehdi; Ehsan, Tahereh Zarrat; Abeshoori, Hoseinali Jafari; Mollajafari, Morteza

doi:10.1007/s40435-023-01142-4

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…A set of variables for the time headway between the respective surrounding vehicles and the subject vehicle ID 𝑛 at time 𝑡, denoted as 𝑿 𝒏 𝒉 (𝑡), is shown in (3). The time headway at time 𝑡, ℎ 𝑛,𝑣𝑒ℎ (𝑡), is calculated by dividing the spacing by actual speed of the following vehicle at time 𝑡, as shown in (4). The subject vehicle becomes the following vehicle when considering the lead or front vehicles (𝑣𝑒ℎ = {1, 2}).…”

Section: Time Headwaymentioning

confidence: 99%

“…Most previous studies on wireless multi-access technology have been conducted to improve traffic safety and the travel comfort of passengers. While Internet-of-Vehicles (IoVs) technology has focused on the operational efficiency of the central control system [1][2][3], vehicle-to-vehicle (V2V) communication technology has concentrated on the driving strategy of individual AVs [4][5][6][7][8]. V2V devices, alongside existing advanced driver assistant systems (ADAS), identify different sensor measurements for the same target to ensure reliable connectivity [9].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication

Lee,

Han,

Lee

et al. 2023

IEEE Access

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With the advent of autonomous vehicles (AVs) and advanced driving assistance systems (ADAS), there has been a growing interest in studying driving behaviors within the field of transportation science. Given that the transition period of mixed traffic is expected to continue for more than 30 years, it is crucial to evolve AV technology to resemble human driving, especially in the freeway weaving sections. Lane-changing (LC) maneuvers in these sections could cause problems for traffic flow, such as traffic breakdown, oscillation, or bottleneck activation. This study proposes an interpretable LC implementation model for naturalistic driving behaviors of AVs based on vehicle-to-vehicle (V2V) communication. To achieve this objective, a systematic selection process is adopted to find optimal V2V features that resemble how human drivers assess LC situations. Based on the minimum redundancy maximum relevance (mRMR) algorithm, seven V2V features have been selected out of 25 candidates. Then, a support vector machine (SVM) is employed to investigate how these features exhibit in each of LC and lane-keeping (LK) situations. The proposed model was applied in a field case of a weaving section on freeway US 101. Performance measures of simple accuracy, precision, recall, and F1-score show high accuracy of 0.9814, 0.9150, 0.7955, and 0.8511, respectively. Subsequently, a strategy for naturalistic LC behaviors of AVs was simulated. The proposed model outperforms high prediction accuracy compared to other existing models. Particularly, errors in the lateral movements have significantly improved. These results suggest that the proposed model effectively simulates naturalistic LC behaviors based on V2V communication.

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Section: Time Headwaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication

Lee,

Han,

Lee

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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Weather-aware fuzzy adaptive cruise control: Dynamic reference signal design

Shojaeefard

Mollajafari

Ebrahimi-Nejad

et al. 2023

Computers and Electrical Engineering

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Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

Mehta,

Padaria,

Bavisi

et al. 2024

IEEE Access

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Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks.VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections. INDEX TERMS ADAS, Advanced Driver Assistance Systems, Attacks, Countermeasures, Defences, Security, Threats I. INTRODUCTION ADAS stands for "Advanced Driver Assistance System". It describes a group of electronic devices and technologies that help drivers operate their cars more safely and effectively. ADAS has numerous electrical and digital tools that aid drivers in minimizing errors and performing parking functions, among other things. The primary goal of this study is to lessen human mistakes and prevent accidents through technologies like automatic emergency braking, collision avoidance, lane-keeping assistance, and autonomous cruise

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Smooth longitudinal driving strategy with adjustable nonlinear reference model for autonomous vehicles

Cited by 5 publications

References 28 publications

Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication

Modeling Lane-Changing Behaviors for Autonomous Vehicles Based on Vehicle-to-Vehicle Communication

Weather-aware fuzzy adaptive cruise control: Dynamic reference signal design

Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

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