Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Saleem, Hajira; Riaz, Farhan; Shaikh, Asadullah; Rajab, Khairan; Rajab, Adel; Akram, Muhammad; Reshan, Mana Saleh Al

doi:10.32604/cmc.2022.022726

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Motivation behind development of deeper architecture was to extract high-level semantic features. Saleem et al [62] also designed steering angle predictive pipeline by utilizing convolution neural network and two metaheuristic algorithms i.e. bat and particle swarm optimizer.…”

Section: B Real-world Data Based Predictorsmentioning

confidence: 99%

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

Saleem,

Asim,

Van Elst

et al. 2024

IEEE Access

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Steering angle controller is a core module of autonomous vehicles, where a slight miscalculation can cause severe accidents. Following safety precautions, development of robust and precise steering angle predictor is an active area of research. However, existing simulator based steering angle predictors lack in predictive performance and have not been evaluated on same benchmark datasets. Furthermore, most of them are evaluated on simulated datasets and their potential on real-world data as well as in cross-domain evaluation of both types data (real-world, simulated) remain unexplored. To accelerate and expedite research related to steering angle prediction contributions of this paper are manifold: 1) It presents two benchmark datasets that are developed using Udacity and CARLA simulators. 2) Following the need for comparative study, over both simulated datasets, it benchmarks the performance of existing predictors under 2 different evaluation settings namely: same-track and cross-track evaluation. 3) In cross-domain evaluation, it explores generalization potential of predictors by training predictors on simulated data and evaluating them on 2 realworld datasets and vice versa. 4) It presents a robust and precise steering angle predictor that utilizes skip connections for proper gradient flow among different convolutional layers. In same-track evaluation where predictors are trained and evaluated on same-track data, proposed predictor outperforms existing predictors by achieving least Mean Absolute Error (MAE) of 0.13, 0.19 and 0.065 over lake track, jungle track and CARLA based datasets, respectively. Similarly, in cross-track evaluation where predictors are trained on one track and are evaluated on other track data, once again proposed predictor outperforms existing predictors by producing average least MAE errors of 0.33 and 0.06 over Udacity and CARLA datasets, respectively. Over two real-world datasets, Sully Chen and Comma.ai, the proposed predictor demonstrates superior performance compared to existing simulator-based predictors, achieving the lowest MAE of 2.41 and 0.50, respectively.

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Section: B Real-world Data Based Predictorsmentioning

confidence: 99%

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

Saleem,

Asim,

Van Elst

et al. 2024

IEEE Access

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show abstract

“…Such predictive cognitive neural networks are often considered the essence of computer vision. They play a critical role in a variety of applications, such as abnormal event detection [1], autonomous driving [2][3][4], intention prediction in robotics [5,6], video coding [7,8], collision avoidance systems [9,10], activity and event prediction [11,12], and pedestrian and traffic prediction [13][14][15]. However, modeling future image content and object motion is challenging due to dynamic evolution and image complexity, such as occlusions, camera movements, and illumination.…”

Section: Introductionmentioning

confidence: 99%

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Ranjan¹,

Bhandari²,

Kim³

et al. 2023

Computers, Materials &Amp; Continua

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Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiplebranch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

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Reducing Parameters by Neuroevolution in CNN for Steering Angle Estimation

Velazco-Muñoz,

Acosta-Mesa,

Mezura-Montes

2024

Lecture Notes in Computer Science

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Cited by 5 publications

References 37 publications

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

RPRP-SAP: A Robust and Precise ResNet Predictor for Steering Angle Prediction of Autonomous Vehicles

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Reducing Parameters by Neuroevolution in CNN for Steering Angle Estimation

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