Abstract:
Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption co...Show MoreMetadata
Abstract:
Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption correlate with each other and to what extent these factors related to connected vehicles can influence the motion prediction performance. The precise recognition of driving behaviors and prediction of the vehicle motion is critical to the driving safety for connected automated vehicles (CAVs). Hence, in this study, an energy-aware driving pattern analysis and motion prediction system are proposed for CAVs using a deep learning-based time-series modeling approach. First, energy-aware longitudinal acceleration and deceleration behaviors and lateral lane-change behaviors are statistically analyzed. Then, a sliding standard deviation (SSD) test is applied to evaluate the smoothness of the trajectory and velocity signals considering different energy consumption levels. An energy-aware personalized joint time-series modeling (PJTSM) approach based on a deep recurrent neural network (RNN) and long short-term memory (LSTM) cell are proposed for accurate motion (trajectory and velocity) prediction of the leading vehicle. Finally, the differences in the prediction performance regarding different energy consumption levels are compared and discussed. It is shown that due to the higher randomness of the driving behaviors, the prediction accuracy for heavy energy users is the lowest among the three categories, which means it is harder to anticipate the driving behaviors of cars exhibiting heavy energy consumption. The personalized estimation of driving behaviors of CAVs will contribute to safer automated driving and transportation systems.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 22, Issue: 7, July 2021)
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- IEEE Keywords
- Index Terms
- Driver Behavior ,
- Driving Behavior Analysis ,
- Neural Network ,
- Energy Consumption ,
- Prediction Accuracy ,
- Predictive Performance ,
- Transport System ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Prediction System ,
- Time Series Models ,
- Energy Usage ,
- Heavy Consumption ,
- Heavy Users ,
- Trajectory Prediction ,
- Vehicle Motion ,
- Motion Prediction ,
- Deep Recurrent Neural Network ,
- Level Of Energy Consumption ,
- Velocity Prediction ,
- Lane Change Maneuver ,
- Lane Change ,
- Precise Prediction ,
- Low Energy Consumption ,
- Differences In Predictions ,
- Long Short-term Memory Network ,
- Prediction Horizon ,
- Feedforward Neural Network Model ,
- Vehicle State ,
- Average Acceleration
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Driver Behavior ,
- Driving Behavior Analysis ,
- Neural Network ,
- Energy Consumption ,
- Prediction Accuracy ,
- Predictive Performance ,
- Transport System ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Prediction System ,
- Time Series Models ,
- Energy Usage ,
- Heavy Consumption ,
- Heavy Users ,
- Trajectory Prediction ,
- Vehicle Motion ,
- Motion Prediction ,
- Deep Recurrent Neural Network ,
- Level Of Energy Consumption ,
- Velocity Prediction ,
- Lane Change Maneuver ,
- Lane Change ,
- Precise Prediction ,
- Low Energy Consumption ,
- Differences In Predictions ,
- Long Short-term Memory Network ,
- Prediction Horizon ,
- Feedforward Neural Network Model ,
- Vehicle State ,
- Average Acceleration
- Author Keywords