Vehicle Control in ADAS Applications

Pérez, Joshue; González, David; Milanés, Vicente; Perallos, Asier; Hernández-Jayo, Unai; Onieva, Enrique; García‐Zuazola, Ignacio Julio

doi:10.1002/9781118894774.ch11

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…The RG and outer-loop controller generate the reference state x = vx ; vy ; r and control input u out (tire traction force) for tracking, respectively. We suppose that the desired longitudinal velocity vx and the steering angle δ are provided from ADAS (Advanced Driver Assistance System) [31]- [33], and steering angle sensors and estimators [34], [35], etc. Based on this assumption, the RG calculates the reference state and the corresponding steady-state control input.…”

Section: Controller Designmentioning

confidence: 99%

Robust Torque Vectoring With Desired Cornering Stiffness for In-Wheel Motor Vehicles

Ryu,

Kim,

Yoo

et al. 2023

IEEE Access

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This paper deals with the torque vectoring control problem for uncertain in-wheel motor vehicles. The main objective of this study is to enhance the cornering performance of vehicles at high speed driving and this is done by generating possibly different motor torques applied to wheels. The proposed controller consists of a model predictive control based outer-loop controller and a disturbance observer based inner-loop controller. The outer-loop controller finds the optimal longitudinal tire force considering state and input constraints. The inner-loop controller estimates the difference between the nominal tire model that is used by the outer-loop controller and the unmodeled disturbance, and compensates the disturbance so that the actual vehicle follows the nominal model. The proposed controller is validated via numerical experiments for circular driving, double lane change, and skidpad scenarios and it is shown that the state tracking performance is improved by using the proposed controller.

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Section: Controller Designmentioning

confidence: 99%

Robust Torque Vectoring With Desired Cornering Stiffness for In-Wheel Motor Vehicles

Ryu,

Kim,

Yoo

et al. 2023

IEEE Access

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“…In the last decade the automotive market has grown the offer and complexity of ADAS [125]. The most requested ADAS in 2009 [126] were Anti-lock braking system and Parking Assistance by Warning (SAE Level 0).…”

Section: B Commercial Initiativesmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

234

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After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles.

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“…Several approaches exist for shared autonomy in terms of control and interfaces. The most common approach is to use advanced driver-assistance systems (ADAS) [12], such as adaptive cruise control. Parallel autonomy vehicle sharedcontrol frameworks, such as [13], [14], produce safe trajectories, while minimizing the deviation from driver inputs.…”

Section: A Related Workmentioning

confidence: 99%

Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections

Huang¹,

McGill²,

Williams

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Predicting the motion of a driver's vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation systems. In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors.Our predictor generates both a conditional variational distribution of future trajectories, as well as a confidence estimate for different time horizons. Our approach allows us to handle inherently uncertain situations, and reason about information gain from each input, as well as combine our model with additional predictors, creating a mixture of experts.We show how to augment the variational predictor with a physics-based predictor, and based on their confidence estimations, improve overall system performance. The resulting combined model is aware of the uncertainty associated with its predictions, which can help the vehicle autonomy to make decisions with more confidence. The model is validated on real-world urban driving data collected in multiple locations. This validation demonstrates that our approach improves the prediction error of a physics-based model by 25% while successfully identifying the uncertain cases with 82% accuracy.

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Vehicle Control in ADAS Applications

Cited by 10 publications

References 13 publications

Robust Torque Vectoring With Desired Cornering Stiffness for In-Wheel Motor Vehicles

Robust Torque Vectoring With Desired Cornering Stiffness for In-Wheel Motor Vehicles

A Review of Sensor Technologies for Perception in Automated Driving

Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections

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