Intelligent adaptive precrash control for autonmous vehicle agents (CBR Engine &amp; hybrid A* path planner)

Elbagoury, Bassant M.; Salem, Abdel-Badeeh M.; Vlădăreanu, Luige

doi:10.1109/icamechs.2016.7813486

Cited by 2 publications

(11 citation statements)

References 10 publications

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“…The second largest number of papers (13) found encompasses studies related to Navigation and Control. They are mostly related to techniques necessary to ensure the proper autonomous navigation and control capabilities required by AVs, such as: remote-controlled semi-AV based on IoT [38]; adaptive pre-crash control [39]; safe trajectory selection [76]; AV following another car driven by a human pilot (Trailing) [40]; safe navigation [41]; heuristic optimization algorithm for unsigned intersection crossing [42]; vehicle coordination [43]; maneuver classification [44]; learning to navigate from demonstration [45]; AV movements optimization in intersection [46]; learning and simulation of the Human Level decisions involved in driving a racing car [47]; path tracking [48]; and fuzzy-logic control approach to manage low level vehicle actuators (steering throttle and brake) [49].…”

Section: B Main Topics Of the Studies (Rq2)mentioning

confidence: 99%

“…As presented previously, they used diverse AI techniques to seek to address a broad range of problems. For example, a hybrid AI architecture encompassing ANN, CBR, and a hybrid Case-Based Planner (A* and D* motion planner) was successfully tested to tackle the precrash problem of intelligent control of autonomous vehicles [39], while SVM was used to support a safest path planning in a dynamic environment to avoid maneuvers too close to an obstacle [41].…”

Section: Reported Findings (Rq5)mentioning

confidence: 99%

“…Here, few examples are presented. The study proposing hybrid control architecture [39] suggests an extension to consider the full kinematics and dynamic limitations of the vehicle, while constantly acting to avoid collisions and unsafe driving. The paper proposing an approach using SVM to avoid maneuvers too close to an obstacle by adding a safety margin [41] proposes future re-search to extend it using a combination with the kinetic convex hulls 2 to enable the possibility of computing the solution ahead in time.…”

Section: E Reported Future Studies (Rq6)mentioning

confidence: 99%

“…The problems related to Machine Control were found in 17 papers (22%). Those problems include: Pre-Crash problem of Intelligent Control of autonomous vehicles robot [39], Safeoptimal trajectory selection for autonomous vehicle [76], Driverless car 100 km experiment [40], Robot maneuvers too close to an obstacle, which increases the probability of an accident. Preventing this is crucial in dynamic environments, where the obstacles, such as other UAVs, are moving [41], Learning and simulation of the Human-Level decisions involved in driving a racing car [47], Control intersection crossing (all way stop) and optimizing it [42], How to prove the correctness of an algorithm for Vehicle Coordination [43], Path tracking [48], Drivers maneuver classification [44], AVs intersections crossing optimization [46] and Manage low level vehicle actuators (steering throttle and brake) [49].…”

Section: Slr Findings Oriented By An Av System Modelmentioning

confidence: 99%

“…PreCrash problem of Intelligent Control of autonomous vehicles robot; Driverless car 100 km experiment Cyber Attack in V2X; Manage low level vehicle actuators (steering throttle and brake); Road and Obstacle Detection; [39], [40], [54], [49], [23] Hidden Markov Based Models 4 7% Vehicle Detection and Counting; Road junction detection; Learn from Demonstration; [36], [29], [45] Estimation Filters 4 7%…”

Section: %mentioning

confidence: 99%

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A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.

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Section: B Main Topics Of the Studies (Rq2)mentioning

confidence: 99%

Section: Reported Findings (Rq5)mentioning

confidence: 99%

Section: E Reported Future Studies (Rq6)mentioning

confidence: 99%

Section: Slr Findings Oriented By An Av System Modelmentioning

confidence: 99%

Section: %mentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Magnitude Adversarial Spectrum Search-based Black-box Attack against Image Classification

Midtlid

Åsheim

2022

Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security

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Recent development has revealed that deep neural networks used in image classification systems are vulnerable to adversarial attacks. In this thesis, we design an untargeted query-efficient decision-based black-box attack against robust image classification models that produce imperceptible adversarial examples. The proposed attack method, Magnitude Adversarial Spectrum Search-based Attack (MASSA), includes two novel components to generate the initial noise and reduce the noise in the frequency domain. Our experiments show that MASSA requires significantly fewer queries than the state-of-the-art HopSkipJumpAttack (HSJA). In addition, MASSA can create adversarial examples with 74, 16% lower l 2 distance than HSJA after only 250 queries. Finally, we demonstrate that MASSA bypasses two defense mechanisms and should be used to evaluate the robustness of future defenses.iii SammendragDe siste årene har forskning vist at dype nevrale nettverk som brukes i bildeklassifiseringssystemer er sårbare mot fiendtlige angrep. I denne oppgaven utformer vi et umålrettet søkeeffektivt beslutningsbasert svart-boks angrep mot robuste bildeklassifiseringsmodeller som produserer skjulte endringer i bilder. Den utviklede angrepsmetoden, Magnitude Adversarial Spectrum Search-based Attack (MASSA), inkluderer blant annet to nyskapende komponenter for å generere den initielle støyen og redusere støyen i frekvensdomenet. Eksperimentene våre viser at MASSA krever betydlig faerre spørringer enn dagens ledende angrep HopSkipJumpAttack (HSJA). I tillegg er MASSA i stand til å produsere fiendtlige bilder med 74, 16% lavere avstand enn HSJA etter kun 250 spørringer. Til slutt demonstrerer vi at MASSA slår to forsvarsmekanismer og bør brukes til å evaluere robustheten til fremtidige forsvar. v Preface This is the Master Thesis written by Kim André B. Midtlid and Johannes Åsheim. The thesis was conducted during the spring semester of 2022 at NTNU. Our work was supervised by Professor Jingyue Li at the Institute of Computer Science and Informatics at NTNU. We would like to thank Li for his continuous assistance and helpful insights. Additionally, we would like to thank friends and family for their motivation and ongoing support throughout our years of study. vii

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Intelligent adaptive precrash control for autonmous vehicle agents (CBR Engine & hybrid A* path planner)

Cited by 2 publications

References 10 publications

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Magnitude Adversarial Spectrum Search-based Black-box Attack against Image Classification

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