Personalized Prediction of Vehicle Energy Consumption Based on Participatory Sensing

Tseng, Chien-Ming; Chau, Chi-Kin

doi:10.1109/tits.2017.2672880

Cited by 32 publications

(29 citation statements)

References 24 publications

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“…A secondary gain is efficiency, especially in the distance-to-empty prediction. By introducing personalization, the prediction error of distance-to-empty can be reduced to 5% [118].…”

Section: Discussionmentioning

confidence: 99%

“…Due to the diversity of driving preferences among different drivers, the accurate evaluation of fuel consumption is a challenging task for intelligent vehicles, especially with plug-in hybrid electric vehicles [22]. To predict fuel use more precisely, various personalized vehicle energy consumption prediction approaches are proposed [32,43,105,112,114,118]. Authors in [105] develop a personalized multi-modality sensing and analysis system, which can efficiently extract information of user-specific driving behaviors and a hybrid electric vehicle operation profile.…”

Section: B Driving Style Recognitionmentioning

confidence: 99%

“…The proposed approach can predict fuel use accurately (0.88-0.996 correlation and 87.8%-89.9% classification accuracy) which is evaluated with realworld experiments. In [112,118], the personalized Distance-To-Empty prediction is achieved by using participatory sensing data. Various approaches are implemented and compared including a speed profile similarity matching approach, a driving habit similarity matching approach and a collaborative filtering approach.…”

Section: B Driving Style Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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“…A secondary gain is efficiency, especially in the distance-to-empty prediction. By introducing personalization, the prediction error of distance-to-empty can be reduced to 5% [118].…”

Section: Discussionmentioning

confidence: 99%

Section: B Driving Style Recognitionmentioning

confidence: 99%

Section: B Driving Style Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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show abstract

“…The expected pseudonym changing energy consumption for cluster member I showed in Figure while transmitting on channel j is estimating use as follows:

E_{exij} = \sum_{z = 1}^{Z} {E_{i}}^{z} X_{i}^{Z} + E_{i} ((), Z) P_{success},

where X

_{i}^{Z}

is the probability that the cluster member i only transmits for Z interval on channel j due to the collision with primary the user.

X_{i}^{Z} = {(1 - {normalX}_{normali})}^{normalL - 1} X_{i} 1 \leq z \leq Z .

…”

Section: Proposed Workmentioning

confidence: 99%

“…When did vehicle node road network transmits information by way of node x, I pc, x is equal to 1,…., m. The pseudonym coefficient of node x is calculated using the pseudonym changing p x to divide by its VPL. 54 Algorithm 1: N = total number of pseudonyms changing 2: P x = pseudonyms changing node x 3: PC = pseudonyms transfer changing traffic information to distance to CH 4: Px = I pc, x 5: distance(PC) = pseudonym changing energy of node x 6: CH(P x ) = cluster head node 7: if (mod(I pc, x ,m)==1) then 8: for each integer m in n do 9: changing pseudonyms(P x ) = (VPL x (i), existing nodes(j)) 10: send (changing ID(i), existing nodes (j)) 11: CH(P x ) = best(changing ID) 12 The expected pseudonym changing energy consumption for cluster member I showed in Figure 5 while transmitting on channel j is estimating use as follows 55,56 :…”

Section: Clustering Protocolsmentioning

confidence: 99%

Distance and clustering‐based energy‐efficient pseudonyms changing strategy over road network

Memon

2018

Int J Communication

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Summary To improve the fairness, the energy consumption changing pseudonyms needs to be taken into account. Existing works focus on changing velocity‐based pseudonyms changing strategy and short changes interval with limited coverage, but due to similar velocity and short changes, internal attacker guesses easily known communication and location information due to location information of vehicle on tracking, which may expose adversary private information, and frequently, pseudonyms changing occurs due to movement of vehicles' similar velocity and short coverage, which may cause serious attack of vehicle. To overcome this problem, distance and cluster can be performed. In this work, we proposed distance and cluster‐based energy pseudonyms changing method for road network. We proposed distance and energy‐based clustering routing service over road network, the cluster head elected to depend on random number of distance and energy to change pseudonyms of vehicles. An each interval to be establish cluster head vehicle deployed while selects the operation mode and informs the cluster members of the selected mode through beacon signal. The cluster head vehicle node performs the pseudonyms changing based on the predicted distance and energy of the cluster member to use clustering optimization. The data of whole network send to report server through these nodes while near the RSU, and the vehicles in this area will use less energy to change the pseudonyms. The simulation results show that the proposed method enhances pseudonyms changing strategy less consumption and delays sufficient privacy level each vehicle also our method has outperform compare with existing methods than we use Sumo simulation and Matlab tools to verify our proposed method. Our proposed method outperformed in terms of pseudonym changing energy efficiency to careful attention during the cluster formation process, stable and balanced clusters that prolong the network lifetime, increases distances to more CH vehicles connectivity to makes clustering group and changing their pseudonyms in terms of high level privacy and finally, CH nodes use Dijkstra's algorithm use MST among the vehicles nodes depend on existing road networks to follow shortest path selection roads in terms of high connectivity probability of CH and stable structure of the network decreases the topology changes and thus,the clustering overhead is reduced.

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Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Thorgeirsson

Vaillant²,

Scheubner

et al. 2020

Softw Pract Exp

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Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud-based module placement reduces the end-to-end latency significantly, when compared with a traditional vehicle-based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

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Personalized Prediction of Vehicle Energy Consumption Based on Participatory Sensing

Cited by 32 publications

References 24 publications

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Distance and clustering‐based energy‐efficient pseudonyms changing strategy over road network

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

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