Vehicle Speed Prediction in a Convoy Using V2V Communication

Jing, Junbo; Kurt, Arda; Ozatay, Engin; Michelini, John; Filev, Dimitar; Özgüner, Ü.

doi:10.1109/itsc.2015.460

Cited by 38 publications

(11 citation statements)

References 13 publications

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“…While, the driving conditions can be easily obtained through the intelligent traffic and wireless communication systems such as global positioning system (GPS) and intelligent transportation system (ITS). Thus, many speed prediction methods take advantages over the acquired global driving conditions to improve the reliability of predicted speed [57,[116][117][118] In terms of speed predictors in ECMSs, Qiu et al [41] employed the vehicle speed target directly as the predicted vehicle speed for ECMS controller. In their research, MPC was utilised to calculate the optimal target velocity profiles based on signal phase and timing (SPAT) information.…”

Section: Speed Predictionmentioning

confidence: 99%

A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

Chen

Liu

et al. 2021

Renewable and Sustainable Energy Reviews

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Section: Speed Predictionmentioning

confidence: 99%

A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

Chen

Liu

et al. 2021

Renewable and Sustainable Energy Reviews

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“…GRU is less complex than the LSTM structure. It combines a forget gate and an input gate using (16)(17)(18)(19). The input gate adjusts the amount of input data added to the previous cell data.…”

Section: ) Gated Recurrent Unitmentioning

confidence: 99%

Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System

et al. 2021

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Short-term prediction models for an ego-vehicle's speed contributes to the improvement of vehicle safety, driveability, and fuel economy. To achieve these desired outcomes, an accurate forward speed prediction model and its successful implementation in a real system is a prerequisite. This paper compares six velocity prediction models based on two types of data-driven models, a Markov chain and a Recurrent Neural Network (RNN), by implementing them in an embedded system to evaluate their prediction accuracy and execution time. The inputs to each model are the driving information acquired on a specific route, such as internal vehicle information, relative speed and distance to the vehicle in the front of the egovehicle, and ego-vehicle's location estimated by the GPS signal along with the B-spline roadway model. The proposed prediction models predict the velocity profile of the ego-vehicle up to the prediction horizon of 150 m. The parameters of the proposed models have been optimized using Hyper-parameter Optimization via Radial basis function and Dynamic coordinate search. By applying real driving data, the Markov chainbased models show slightly lower prediction accuracy but shorter execution time than those of the RNNbased models. INDEX TERMS Embedded system, execution time, gated recurrent unit (GRU), long short-term momory (LSTM), Markov chain, prediction accuracy, prediction algorithm, recurrent neural network (RNN)

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“…The results show that the non-parametric approaches, such as GMR and ANN models, were better than the parametric models for all the tested prediction horizons. Jing et al [ 27 ] proposed a method to predict speed in Cooperative Adaptive Cruise Control (CACC) using V2V communication. The general idea of the speed prediction algorithm is to estimate the propagation characteristics of speed perturbations from the lead vehicle to the preceding vehicle for a given convoy, so that the preceding vehicle’s speed changes corresponding to a given perturbation can be calculated as soon as it acts on the lead vehicle.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Collision Prediction under Reduced Visibility Conditions

Chen

Hsiung

2018

Sensors

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Rear-end collisions often cause serious traffic accidents. Conventionally, in intelligent transportation systems (ITS), radar collision warning methods are highly accurate in determining the inter-vehicle distance via detecting the rear-end of a vehicle; however, in poor weather conditions such as fog, rain, or snow, the accuracy is significantly affected. In recent years, the advent of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication systems has introduced new methods for solving the rear-end collision problem. Nevertheless, there is still much left for improvement. For instance, weather conditions have an impact on human-related factors such as response time. To address the issue of collision detection under low visibility conditions, we propose a Visibility-based Collision Warning System (ViCoWS) design that includes four models for prediction horizon estimation, velocity prediction, headway distance prediction, and rear-end collision warning. Based on the history of velocity data, future velocity volumes are predicted. Then, the prediction horizon (number of future time slots to consider) is estimated corresponding to different weather conditions. ViCoWs can respond in real-time to weather conditions with correct collision avoidance warnings. Experiment results show that the mean absolute percentage error of our velocity prediction model is less than 11%. For non-congested traffic under heavy fog (very low visibility of 120 m), ViCoWS warns a driver by as much as 4.5 s prior to a possible future collision. If the fog is medium with a low visibility of 160 m, ViCoWs can give warnings by about 2.1 s prior to a possible future collision. In contrast, the Forward Collision Probability Index (FCPI) method gives warnings by only about 0.6 s before a future collision. For congested traffic under low visibility conditions, ViCoWS can warn a driver by about 1.9 s prior to a possible future collision. In this case, the FCPI method gives 1.2 s for the driver to react before collision.

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Vehicle Speed Prediction in a Convoy Using V2V Communication

Cited by 38 publications

References 13 publications

A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles

Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System

Vehicle Collision Prediction under Reduced Visibility Conditions

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