Autonomous highway driving using reinforcement learning with safety check system based on time-to-collision

Nie, Xiaotong; Liang, Yupeng; Ohkura, Kazuhiro

doi:10.1007/s10015-022-00846-8

Cited by 6 publications

(2 citation statements)

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“…In the calculation of the desired relative spacing

, Time-To-Collision (in this paper,

is used to represent TTC) is a crucial parameter [ 25 , 26 , 27 ]. The physical significance of

is the time interval for possible collision calculated through the kinematic information between the vehicle and the forward obstacle, as shown in Equation ( 22 ).…”

Section: Acc System Control Strategy Developmentmentioning

confidence: 99%

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Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

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“…In the calculation of the desired relative spacing

, Time-To-Collision (in this paper,

is used to represent TTC) is a crucial parameter [ 25 , 26 , 27 ]. The physical significance of

is the time interval for possible collision calculated through the kinematic information between the vehicle and the forward obstacle, as shown in Equation ( 22 ).…”

Section: Acc System Control Strategy Developmentmentioning

confidence: 99%

“…Using a weighted matrix to adjust the prediction error to improve the system prediction accuracy, Equation ( 24) is transformed into Equation (27).…”

Section: Acc System Follows Mode Control Strategymentioning

confidence: 99%