A predictive control framework for torque-based steering assistance to improve safety in highway driving

Ercan, Ziya; Carvalho, Ashwin; Tseng, H. Eric; Gökaşan, Metin; Borrelli, Francesco

doi:10.1080/00423114.2017.1337915

Cited by 40 publications

(26 citation statements)

References 21 publications

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“…Other works have used these angles to model the driver as a proportional controller (∝ [θ n , θ f ]) [123]. Others have used a proportional model with respect to the lateral and angular errors ([y L , ψ L ]) [113], [122]. Additionally, these driver models have been used as driver agents in numerical simulations [123].…”

Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

“…Sentouh et al [63] presented the first work using this approach in 2010, with the use of a linear quadratic regulator (LQR) controller that reduced the system intervention in a path-following task. This work paved the way toward different strategies using DRV models along with optimal control techniques, such as H 2 optimal control [23], H 2 preview control [109], [120], and MPC [113], [135]. Also, LMI optimization control is a common approach [64], combined with a T-S fuzzy modeling technique to deal with the variable parameters of the system [54], [112], [119].…”

Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

“…Only one system [113] was tested on a real vehicle, and then only at a low speed (36 km/h). The rest were tested in DiL simulators at an average speed of 70 km/h.…”

Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Shared Control for Automated Vehicles: Theory and Applications

Marcano

Díaz

Pérez

et al. 2020

IEEE Trans. Human-Mach. Syst.

156

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The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory-and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driverin-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.

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Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

Section: B Model-based Coupled Shared Controllersmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Shared Control for Automated Vehicles: Theory and Applications

Marcano

Díaz

Pérez

et al. 2020

IEEE Trans. Human-Mach. Syst.

156

View full text Add to dashboard Cite

show abstract

“…As it has been noted in [12,17,46], the driver's behaviors can be very useful for improving control performance, especially in a predictive control framework. To predict the driver's behaviors, in this paper, we utilize the predicted trajectory of the host vehicle to obtain the input intention of the driver via solving an MPC optimization problem.…”

Section: Prediction Of Driver's Behaviorsmentioning

confidence: 99%

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

Jiang

Zhang

et al. 2021

Sci. China Inf. Sci.

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With the increasing number of cars, road traffic accidents have caused a lot of losses every year and human factors play an important role in many cases. Applying active safety assistance control or shared control techniques in intelligent vehicles is promising to reduce the number of traffic accidents. In this context, the dynamic optimization of the shared control policy and the smooth transitions of control authority between human drivers and intelligent driving systems are critical issues to be solved. Motivated by this, this paper proposes an event-triggered shared control approach for safe-maneuver of intelligent vehicles with online risk assessment. In the proposed approach, a Bayesian regularized artificial neural network (BRANN) is designed to predict vehicle trajectories and build a quantization function to assess the risk level owing to potential collision events. The shared controller dynamically optimizes the shared control policies between the human and the intelligent driving system via solving a model predictive control (MPC) problem. The predicted driving behaviors in the prediction horizon are pre-computed with a finite-horizon model predictor steering the predicted trajectories contributed by human driving. Moreover, smooth transitions back to human driving mode are realized via adding penalties on the shared control of the intelligent driving system. Three simulation scenarios in the PreScan environment, i.e., rear-end collision avoidance, lane-keeping and unskilled driving, are studied to test the effectiveness of the proposed approach. The simulation results, including the comparison with a linear quadratic regulator (LQR)-based shared controller, are reported, which show that the proposed approach can timely evaluate dangerous events and realize safe driving in terms of collision avoidance and lane-keeping. Also, the proposed approach outperforms the LQR-based shared controller in terms of smooth transitions.

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“…As multiple control objectives emerge from the vehicle's point of view, e.g. trajectory tracking and complying to the human driver, model predictive control [13, 14] and linear‐quadratic control [9] have been used in the driver‐machine shared control system [15]. However, the driver's model can be different across individuals and in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning of an unknown road path through cooperative driving of vehicles

Yang

Huang

et al. 2020

IET Intelligent Trans Sys

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This study proposes a method for a vehicle controller to learn human driving behaviours through iterative interactions. In particular, the vehicle controller and the human driver jointly control a vehicle along a path only known to the human driver. Through repeated cooperative driving, the vehicle controller estimates the hidden desired path of the driver by minimising the control input. Eventually, semi‐autonomous driving is realised since the vehicle controller is able to automatically track the target path and release the human driver from the driving task. The iterative learning of the human target path on the basis of the proposed algorithm is in the spatial domain, and is effective in the presence of uncertain human driving speeds. The validity of the proposed method is proved by rigorous analysis and demonstrated by numerical simulations.

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A predictive control framework for torque-based steering assistance to improve safety in highway driving

Cited by 40 publications

References 21 publications

A Review of Shared Control for Automated Vehicles: Theory and Applications

A Review of Shared Control for Automated Vehicles: Theory and Applications

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

Iterative learning of an unknown road path through cooperative driving of vehicles

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