Survey on Control Schemes for Automated Driving on Highways

Rupp, Astrid; Stolz, Michael

doi:10.1007/978-3-319-31895-0_4

Cited by 40 publications

(33 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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]. However, it suffers from a few drawbacks namely: (i) performance is sensitive to the sampling rate of the controller (ii) chattering problems, (iii) robustness only on the sliding surface, and (iv) needs prior knowledge of disturbance and uncertainty bounds [77,82,83]. [38, 46-48, 50, 51, 57].…”

Section: Cell Decomposition Algorithms Such As Rapidly-exploring Randmentioning

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%

“…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]. Pure-pursuit and Stanley method are two prevalent geometric controllers[77,[80][81][82]. Pure-pursuit is a technique where the vehicle is in constant pursuit of a virtual moving point in front of the vehicle and 'Stanley' controller is based on non-linear geometric controller which considers heading and lateral error to compute steering angle corrections[77].…”

mentioning

confidence: 99%

See 3 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

187

View full text Add to dashboard Cite

Section: Cell Decomposition Algorithms Such As Rapidly-exploring Randmentioning

confidence: 99%

Section: Cell Decomposition Algorithms Such As Rapidly-exploring Randmentioning

confidence: 99%

mentioning

confidence: 99%

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

187

View full text Add to dashboard Cite

“…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

140

View full text Add to dashboard Cite

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.

show abstract

Adaptive path planning and tracking system based on model predictive control for autonomous vehicle local obstacle avoidance

Zhang,

Ge,

Pan

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

Path planning and tracking control are the key technologies of autonomous vehicle. The planned path and tracking results affect driving stability and safety directly. In this paper, an improved local path planning method based on model predictive control is proposed to match the variation of vehicle speed and road adhesion coefficient. A two‐layer model predictive control (MPC) path planning and tracking system is further designed to validate the method and the simulation results show that the proposed solution solves the problem of excessive avoidance and reduces the lateral deviation with the reference path.

show abstract

Survey on Control Schemes for Automated Driving on Highways

Cited by 40 publications

References 79 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

Towards connected autonomous driving: review of use-cases

Adaptive path planning and tracking system based on model predictive control for autonomous vehicle local obstacle avoidance

Contact Info

Product

Resources

About