Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

Amer, Noor Hafizah; Zamzuri, Hairi; Hudha, Khisbullah; Kadir, Zulkiffli Abdul

doi:10.1007/s10846-016-0442-0

Cited by 314 publications

(177 citation statements)

References 66 publications

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“…Without considering vehicle dynamics such as friction forces, tire slips and energy, geometric and kinematic control algorithms can lead to risky driving behaviour at high speeds where dynamics significantly influence the vehicle's motion, such as during sudden lane changes or attempts to avoid unexpected obstacles [138,140]. In the application of the "Pure Pursuit" geometric algorithm, where the vehicle is "in constant pursuit of a virtual moving point" [138], "rapid changes" in the vehicle's path during high-speed driving can cause the algorithm to "overestimate" the system's ability to produce steering inputs to correct the vehicle's movement, resulting in excessive steering and skidding of the rear vehicle [51,138]. Furthermore, setting control parameters to "compensate" for the neglect of dynamics renders geometric and kinematic control algorithms highly sensitive to parameter variations [139].…”

Section: Controlmentioning

confidence: 99%

“…These hardware components interact with the AV's software, which comprises perception, decision-making (or planning) and control. Perception refers to the collection of information and knowledge from the environment by the AV system through its sensors and communication networks; Decision-making enables the AV to meet its goals through processes including mission planning that involves decision-making to meet "high-level objectives" such as deciding which route to take, behavioural planning that involves producing "local objectives" such as switching lanes and overtaking, and motion (or local) planning that produces the steps required to achieve local objectives, such as reaching a target destination [37], whereby algorithms make decisions based on selected preferences and criterion; and control algorithms execute these decisions by calculating the inputs for the AV's actuators, such as the steering angle and vehicle speed, for the AV to follow a given trajectory [50,51]. The proper functioning of algorithms in all software and hardware components is critical for the AVs' safe operation.…”

mentioning

confidence: 99%

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Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

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Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.Sustainability 2019, 11, 5791 2 of 28 surroundings and algorithms that process the data, make, and execute driving decisions through the vehicle's actuators. The quality of data retrieved by the AV's sensors is critical for decision-making [21], and the efficiency, precision and reliability of decision-making algorithms allows AVs to surpass the typical human driver in performing driving tasks [22]. A major component in AVs is machine-learning (ML) [23] algorithms that continuously learn and adapt to new information, which is essential for responding to unexpected situations and providing on-demand transportation services [24]. Algorithms' independence from human input and ML's data-driven nature allows AVs to significantly reduce or eliminate human errors that have been responsible for 90% of road fatalities, such as speeding, alcohol impairment, distractions and induced fear [25,26].However, an overemphasis on technological solutions alone for economic development could risk neglecting social and environmental considerations and thus hinder true "smartness" [27]. Scholars have cautioned against rushing to develop smart mobility solutions such as AVs without being prepared to manage their potential "negative externalities" [1,28]. In particular, issues in algorithmic decision-making in AVs can have undesirable effects on safety and equity. Firstly, data mining processes in AVs are susceptible to biases that lead algorithms to prioritise the safety of certain groups of road users over others and thus, perpetuate discrimination [29,30]. Secondly, many scholars stress the need to design algorithms with ethical considerations to ensure that AVs make ethical driving dec...

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Section: Controlmentioning

confidence: 99%

mentioning

confidence: 99%

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Lim

Taeihagh

2019

Sustainability

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“…Atsižvelgiant į pasirenkamas prielaidas ir taikomus valdymo apribojimus, judėjimo kontroliavimo uždaviniai skirstomi į atitinkamas grupes (Kachroo, Mellodge 2005). Skirtingos kontroliavimo užda-vinių grupės gali būti apibendrinamos nusakant tokį patį jų tikslą -užtikrinti tikslų pageidaujamos judėjimo trajektorijos sekimą kontroliuojant skersinį ir / arba išilginį judėjimą (Amer et al 2016). Kontroliavimo uždavinių sprendimas laikomas viena iš sudėtingiausių užduočių, kuriant ir tobulinant autonomines transporto priemones, todėl racionalių valdymo algoritmų formulavimas bei jų analizė yra reikš-mingas dėmuo, siekiant užtikrinti sklandesnį, tikslesnį ir saugesnį autonominių transporto priemonių judėjimą įvai-riomis trajektorijomis ir sąlygomis.…”

Section: įVadasunclassified

“…non-holonomic) sistema (Katrakazas et al 2015;Amer et al 2016). Valdymo teorijoje laikoma, kad nagrinėjama sistema yra holonominė tuomet, jeigu jos bendras nepriklausomų kintamųjų -laisvės laipsnių skaičius -atitinka reliatyviųjų judesių, kuriuos galima kontroliuoti, skaičių (Katrakazas et al 2015).…”

Section: Judėjimo Trajektorijos Optimizavimasunclassified

Formulation and Theoretical Analysis of the Control Algorithm of the Autonomous Vehicle

Skačkauskas

Mejeras

Sokolovskij

2017

Science - Future of Lithuania

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2017 © Straipsnio autoriai. Leidėjas VGTU leidykla "Technika". Šis straipsnis yra atvirosios prieigos straipsnis, turintis Kūrybinių bendrijų (Creative Commons) licenciją (CC BY-NC 4.0), kuri leidžia neribotą straipsnio ar jo dalių panaudą su privaloma sąlyga nurodyti autorių ir pirminį šaltinį. Straipsnis ar jo dalys negali būti naudojami komerciniams tikslams. parametrai vis labiau nukrypsta nuo nustatytų. Remiantis šiuo apibrėžimu galima teigti, kad autonominės transporto priemonės judėjimas yra nestabilus, jeigu judėjimo metu, nustojus veikti išoriniams veiksniams, esamos realiojo judė-jimo koordinatės vis labiau nutolsta nuo pageidaujamų, t. y. didėja koordinačių nuokrypio vektoriaus elementų reikš-mės. Tokiu atveju naudojamas valdymo algoritmas turi įver-tinti nuokrypos dydį, t. y. koordinačių nuokrypio vektoriaus elementų reikšmes ir, atsižvelgiant į taikomą optimizavimo kriterijų ar jų sistemą, priimti racionalų ir patikimą valdymo sprendimą, kuris užtikrintų nuokrypos panaikinimą ar sumažinimą. Taigi yra aišku, kad, siekiant užtikrinti kuo didesnį judėjimo tikslumą ir stabilumą, veikiant įvairiems išoriniams veiksniams, autonominės transporto priemonės analizuojamos kaip tarpdisciplininis objektas, išskiriant ryšius tarp transporto priemonių dinamikos ir automatinio valdymo teorijos. MOKSLAS -LIETUVOS ATEITIS SCIENCE -FUTURE OF LITHUANIAChappell (1983) vienas pirmųjų autonominę transporto priemonę aprašė ne kaip vientisą, o kaip kompleksinį tyrimų objektą, sudarytą iš skirtingų sistemų, iš kurių kiekviena atlieka atskiras funkcijas, tačiau sąveikauja tarpusavyje. Tik matematiniu modeliavimu paremtas intelektinis valdymo algoritmas buvo pritaikytas mokslininkų Hogle ir Bonissone (1984 Reikšminiai žodžiai: autonominis automobilis, genetinis algoritmas, judėjimo trajektorijos sekimas, nuokrypis.

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“…However, the nonlinearity of vehicle and tire models leads to a high computational burden [4]. A bicycle model with a small-angle assumption and a proportional linear tire model are widely used in path-tracking research [5,6].…”

Section: Introductionmentioning

confidence: 99%

Design of a Path-Tracking Steering Controller for Autonomous Vehicles

Sun

Xin

et al. 2018

Energies

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This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire's lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.

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Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

Cited by 314 publications

References 66 publications

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Formulation and Theoretical Analysis of the Control Algorithm of the Autonomous Vehicle

Design of a Path-Tracking Steering Controller for Autonomous Vehicles

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