Experimental Validation of a Steering Control System using an Adaptive Fuzzy Controller and Computer Vision

Sato, Thiago H.; Santos, Sergio Ronaldo Barros dos; Oliveira, A.; Cappabianco, Fábio A. M.; Givigi, Sidney N.

doi:10.1109/syscon48628.2021.9447097

Cited by 1 publication

(1 citation statement)

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“…Moreover, authors presented a novel bounded comparable function based on kinematic and Taylor series expansion for the steering control problem. In another research, Yu et al [30] presented a nonlinear model predictive control (NMPC) based real wheel steering controller. The authors compered the proposed controller with other variants of NMPC, with the rear steering angle equal to front or zero.…”

Section: B Steering Controllermentioning

confidence: 99%

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Rasib¹,

Butt²,

Riaz³

et al. 2021

IEEE Access

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With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving vehicles. Till recently, numerous lane-marking detection based steering control and lane keeping methods have been proposed to perform autonomous driving on urban well-structured roads. But the matter of fact is that these methods are not feasible on roads where lane markings are not available or faded over time which makes drivable road region detection a crucial task. Also, it is highly difficult task to estimate the steering angle on such highly deteriorated roads using existing road detection and steering angle estimation methods. To the best of our knowledge, there is no standard benchmark available for drivable road region detection and steering angle estimation on unstructured roads. To this end, we present a large-scale dataset for drivable road detection, comprising of 15,000-pixel level high-quality fine annotations. Also, we present a novel end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on unstructured roads. The proposed method performs pixel-level segmentation to extract drivable road region and calculates the lane interception to estimate the steering angle of self-driving vehicles. Lastly, a comprehensive qualitative and quantitative analysis has been carried out to demonstrate the effectiveness of our proposed dataset, road detection and steering angle estimation method. Our benchmark is available at https://carl-dataset.github.io/index/.

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Section: B Steering Controllermentioning

confidence: 99%