Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects

Dixit, Shilp; Fallah, Saber; Montanaro, Umberto; Dianati, Mehrdad; Stevens, Alan; Mccullough, Francis; Mouzakitis, Alexandros

doi:10.1016/j.arcontrol.2018.02.001

Cited by 186 publications

(100 citation statements)

References 66 publications

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“…Control algorithms and their underlying models of vehicle motion have been developed with considerable success for trajectory tracking, which ensures that the AV moves along the path determined by its decision-making algorithms [136,137]. Many studies refer to "control algorithms" as "controllers" or "control strategies" [22,137,138]. However, safety risks can arise from control algorithms' potential inaccuracies in modelling the AV's motion, particularly amid unexpected road conditions.…”

Section: Controlmentioning

confidence: 99%

“…However, safety risks can arise from control algorithms' potential inaccuracies in modelling the AV's motion, particularly amid unexpected road conditions. Geometric and kinematic control algorithms are recognised for their simplicity and relatively low computational cost [139], but as they only model the vehicle's geometrical dimensions and kinematic properties such as acceleration and velocity [138], they can lead to inaccuracies and vehicle instability due to their neglect of vehicle dynamics. 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].…”

Section: Controlmentioning

confidence: 99%

“…Geometric and kinematic control algorithms are recognised for their simplicity and relatively low computational cost [139], but as they only model the vehicle's geometrical dimensions and kinematic properties such as acceleration and velocity [138], they can lead to inaccuracies and vehicle instability due to their neglect of vehicle dynamics. 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].…”

Section: Controlmentioning

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%

Section: Controlmentioning

confidence: 99%

Section: Controlmentioning

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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“…Recently, deep learning has gained attention due to the numerous state-of-the-art results it has achieved in fields such as image classification and speech recognition [24]- [26]. This has led to increasing use of deep learning in autonomous vehicle applications, including planning and decision making [27]- [31], perception [32]- [36], as well as mapping and localisation [37]- [39]. The performance of Convolutional Neural Networks (CNNs) with raw camera inputs has the potential to reduce the number of sensors used by autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Kuutti

Bowden

Jin

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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451

182

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Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

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“…Full vehicle models and nonlinear tire models are usually used to simulate the vehicle response during high speeds and large-steering-angle driving [3]. However, the nonlinearity of vehicle and tire models leads to a high computational burden [4].…”

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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Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects

Cited by 186 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

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Design of a Path-Tracking Steering Controller for Autonomous Vehicles

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