Recent Trends in Autonomous Vehicle Validation Ensuring Road Safety with Emphasis on Learning Algorithms

Abraham, Anuj; Nagavarapu, Sarat Chandra; Prasad, Shitala; Vyas, Pranjal; Mathew, Libin K.

doi:10.1109/icarcv57592.2022.10004304

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Equation (15) can also be simplified to Eq. ( 16) By substituting the constant factor Y cl i m shown in Eq.…”

Section: Proposed Modelmentioning

confidence: 99%

“…Although smart cities adopt ITS and AVs, the key operational challenge is to accurately match taxi supply with passenger demand to minimize waiting times and optimize the utilization of ATs. Machine learning (ML) has made significant contributions to the development of forecasting models, providing improved performance and cost-effective solutions in the AVs industry [15][16][17] . Passenger demand forecasting is an integral component of the ATs service, as it enables the efficient allocation of taxis to passengers.…”

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confidence: 99%

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A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Munawar,

Piantanakulchai

2024

Sci Rep

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The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared ($$R^2$$ R 2 ). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available.

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“…Equation (15) can also be simplified to Eq. ( 16) By substituting the constant factor Y cl i m shown in Eq.…”

Section: Proposed Modelmentioning

confidence: 99%

mentioning

confidence: 99%

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Munawar,

Piantanakulchai

2024

Sci Rep

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show abstract

“…Moreover, this approach aligns with the contemporary automotive trend, emphasizing connected and autonomous vehicle technologies, specifically targeting enhanced energy efficiency and prolonged battery life. In recent years, the potential of connected and autonomous vehicles has grown significantly due to current technologies, enabling vehicle efficiency improvements [15,16] while maintaining high safety standards [17]. Connectivity is pivotal in enhancing vehicles' awareness of their surroundings and robustness in predicting external actions [18].…”

Section: Usa Electric Vehicle's Market Sharementioning

confidence: 99%

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Fiorillo,

Mauro,

Biswas

et al. 2024

Energies

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Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

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Real-Time Traffic Accident Detection for an Intelligent Mobility in Smart Cities

Abraham,

Math,

Prasad

et al. 2023

Signals and Communication Technology

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Recent Trends in Autonomous Vehicle Validation Ensuring Road Safety with Emphasis on Learning Algorithms

Cited by 6 publications

References 26 publications

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Real-Time Traffic Accident Detection for an Intelligent Mobility in Smart Cities

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