A Robust Energy and Emissions Conscious Cruise Controller for Connected Vehicles with Privacy Considerations

Huang, Chunan; Zhang, Xueru; Salehi, Rasoul; Ersal, Tulga; Stefanopoulou, Anna G.

doi:10.23919/acc45564.2020.9147406

Cited by 2 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, one approach is to improve fuel efficiency by reducing time-headway for a single vehicle to its predecessor, or form platoons for a group of vehicles to reduce energy loss by air drag reduction [1][2][3][4] . Another commonly used approach is to formulate the selection of the sequence of control actions as an optimal control problem to reduce total fuel or energy consumption while including the real-time feedback information and preview information of the surrounding traffic [5][6][7][8][9][10][11][12][13][14][15][16] . For instance, fuel consumption has been included in optimization objectives in Refs.…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop exploration of energy versus emissions trade-off in eco-following scenarios for connected automated vehicles

Huang

Salehi

Stefanopoulou

et al. 2022

International Journal of Engine Research

Self Cite

View full text Add to dashboard Cite

Studies on eco-driving have mostly taken an energy-centric view and considered driving performance, while less attention has been paid on emissions behavior. This work extends in an experimentally verified way our understanding of the trade-offs among fuel economy, driving aggressiveness, and, especially, emissions in connected automated diesel-powered vehicles. Experiments are performed with a 6.7-L Ford Powerstroke diesel engine, a urea-SCR based NOx aftertreatment system, and a full model for a Ford F250 medium-duty truck in the loop to provide realistic assessment of fuel consumption, tailpipe emissions, and driving style performances. An energy and emissions conscious speed planner is leveraged to follow the traffic. This planner offers flexibility in prioritizing energy or emissions while satisfying user-defined headway constraints, and thus allows exploration of different calibrations in a unified way. Results show how various calibrations of the flexible leader following policy yield 8%–14% decrease in total fuel consumption and 64%–70% decrease in tailpipe emissions compared with a strictly constrained following policy.

show abstract

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop exploration of energy versus emissions trade-off in eco-following scenarios for connected automated vehicles

Huang

Salehi

Stefanopoulou

et al. 2022

International Journal of Engine Research

Self Cite

View full text Add to dashboard Cite

show abstract

Differentially Private Real-Time Release of Sequential Data

Zhang

Khalili

Liu

2022

ACM Trans. Priv. Secur.

Self Cite

View full text Add to dashboard Cite

Many data analytics applications rely on temporal data, generated (and possibly acquired) sequentially for online analysis. How to release this type of data in a privacy-preserving manner is of great interest and more challenging than releasing one-time, static data. Because of the (potentially strong) temporal correlation within the data sequence, the overall privacy loss can accumulate significantly over time; an attacker with statistical knowledge of the correlation can be particularly hard to defend against. An idea that has been explored in the literature to mitigate this problem is to factor this correlation into the perturbation/noise mechanism. Existing work, however, either focuses on the offline setting (where perturbation is designed and introduced after the entire sequence has become available), or requires a priori information on the correlation in generating perturbation. In this study we propose an approach where the correlation is learned as the sequence is generated, and is used for estimating future data in the sequence. This estimate then drives the generation of the noisy released data. This method allows us to design better perturbation and is suitable for real-time operations. Using the notion of differential privacy, we show this approach achieves high accuracy with lower privacy loss compared to existing methods.

show abstract

A Robust Energy and Emissions Conscious Cruise Controller for Connected Vehicles with Privacy Considerations

Cited by 2 publications

References 24 publications

Hardware-in-the-loop exploration of energy versus emissions trade-off in eco-following scenarios for connected automated vehicles

Hardware-in-the-loop exploration of energy versus emissions trade-off in eco-following scenarios for connected automated vehicles

Differentially Private Real-Time Release of Sequential Data

Contact Info

Product

Resources

About