Path Tracking for Automated Driving: A Tutorial on Control System Formulations and Ongoing Research

Sorniotti, Aldo; Barber, Phil; Pinto, Stefano De

doi:10.1007/978-3-319-31895-0_5

Cited by 31 publications

(34 citation statements)

References 75 publications

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“…However, at high-speeds and/or under low road friction overtaking scenarios, it is quite possible that the system (i.e., vehicle, and tires) may exhibit significant non-linear behaviour and therefore for appropriate scenarios either nonlinear models, linear parameter varying (LPV) models or multiple models can be used to capture the relevant dynamic behaviour of the system [78,79]. For a detailed review of different vehicle models the reader is directed towards the work by [77,[80][81][82]. state-feedback controller has also been used to design vehicle trajectory tracking controllers and shows good tracking accuracy due to the non-linear control law [77,83].…”

Section: Cell Decomposition Algorithms Such As Rapidly-exploring Randmentioning

confidence: 99%

“…However, in the absence of formal stability proofs and exception handling, such approaches cannot be suggested for real-world implementation [81,85]. The advantages and disadvantages of the different controllers discussed above are summarised in Table 2 It is noteworthy that the study of potential benefits that can be achieved by leveraging off-board information via V2X communication systems for autonomous trajectory planning and tracking is in a nascent stage and marks a new chapter of study in the field of autonomous vehicles.…”

Section: Tracking Controllersmentioning

confidence: 99%

“…comparison of different tracking controllers for autonomous vehicles was performed in[77,[80][81][82]. Some relevant observations of these comparisons along with other examples of tracking controllers for autonomous overtaking are discussed below.Geometric controllers are designed using geometric vehicle models[77, 80- 82].…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Dixit

Fallah

Montanaro

et al. 2018

Annual Reviews in Control

197

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Section: Cell Decomposition Algorithms Such As Rapidly-exploring Randmentioning

confidence: 99%

Section: Tracking Controllersmentioning

confidence: 99%

See 1 more Smart Citation

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

Dixit

Fallah

Montanaro

et al. 2018

Annual Reviews in Control

197

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“…Autonomous vehicles require path tracking controllers (PTCs) that ensure safe behavior even in extreme maneuvers. Most of the path tracking (PT) studies (see the survey in [1]) focus on steering actuation. A large body of literature on steering control for PT was initially developed with the purpose of driver modeling [2]- [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Path Tracking and Torque-Vectoring Controllers for Autonomous Electric Vehicles

Chatzikomis¹,

Sorniotti

Gruber

et al. 2018

IEEE Trans. Intell. Veh.

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Steering control for path tracking in autonomous vehicles is well documented in the literature. Also, continuous direct yaw moment control, i.e., torque-vectoring, applied to human-driven electric vehicles with multiple motors is extensively researched. However, the combination of both controllers is not yet well understood. This paper analyzes the benefits of torquevectoring in an autonomous electric vehicle, either by integrating the torque-vectoring system in the path tracking controller, or through its separate implementation alongside the steering controller for path tracking. A selection of path tracking controllers is compared in obstacle avoidance tests simulated with an experimentally validated vehicle dynamics model. A genetic optimization is used to select the controller parameters. Simulation results confirm that torque-vectoring is beneficial to autonomous vehicle response. The integrated controllers achieve the best performance if they are tuned for the specific tire-road friction condition. However, they can also cause unstable behavior when they operate in lower friction conditions without any retuning. On the other hand, separate torque-vectoring implementations provide consistently stable cornering response for a wide range of friction conditions. Controllers with preview formulations, or based on appropriate reference paths with respect to the middle line of the available lane, are beneficial to the path tracking performance.

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“…Out of the wide variety of vehicle models available in literature a kinematic bicycle model and dynamic bicycle model have been found to provide a good compromise between model complexity and accuracy for controller design related to highway driving applications [85,86]. For a more detailed discussion on different vehicle models the reader is directed towards the work by [81,[87][88][89].…”

Section: Lane Changementioning

confidence: 99%

Towards connected autonomous driving: review of use-cases

Montanaro

Dixit

Fallah

et al. 2018

Vehicle System Dynamics

150

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Connected autonomous vehicles are considered as mitigators of issues such as traffic congestion, road safety, inefficient fuel consumption, and pollutant emissions that current road transportation system suffers from. Connected Autonomous vehicles utilise communication systems to enhance the performance of autonomous vehicles and consequently improve transportation by enabling cooperative functionalities, namely, cooperative sensing and cooperative manoeuvring. The former refers to the ability to share and fuse information gathered from vehicle sensors and road infrastructures to create a better understanding of the surrounding environment while the latter enables groups of vehicles to drive in a co-ordinated way which ultimately results in a safer and more efficient driving environment. However, there is a gap in understanding how and to what extent connectivity can contribute in improving the efficiency, safety and performance of autonomous vehicles. Therefore, the aim of this paper is to investigate the potential benefits that can be achieved from connected autonomous vehicles through analysing five use-cases: (i)vehicle platooning, (ii) lane changing, (iii) intersection management, (iv) intersection management, and (v) road friction estimation. The current paper highlights that although connectivity can enhance the performance of autonomous vehicles and contribute to improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, the number of autonomous vehicles, traffic scenarios, and the way of augmenting off-board information into vehicle control systems.

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Path Tracking for Automated Driving: A Tutorial on Control System Formulations and Ongoing Research

Cited by 31 publications

References 75 publications

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

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

Comparison of Path Tracking and Torque-Vectoring Controllers for Autonomous Electric Vehicles

Towards connected autonomous driving: review of use-cases

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