Electronic Stability Program (ESP)

Kost, Friedrich; Schuh, Jürgen; Koch-Dücker, Heinz-Jürgen; Niewels, Frank; Ehret, Thomas; Wagner, Jochen; Papert, Ulrich; Eberspächer, Peter; Heinen, Frank

doi:10.1007/978-3-658-03975-2_16

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, pre-calibration of MCP thresholds are necessary for such a strategy, which are not able to deal with different driving styles and driving scenarios, hence it is suboptimal and less helpful for developing a personalized braking system [19]. Besides, MCP is an important control-oriented parameter that is useful and efficient in EBD, ABS, ESP, etc., [8,9]. Therefore, MCP is initially fed to FCM for offline training and brake intention labeling.…”

Section: Hybrid Machine Learning Frameworkmentioning

confidence: 99%

“…Additionally, efficient recognition of braking intention and intensity are beneficial to shorten the action time of actuators and optimize the regenerative braking strategy [5] and downshift the schedule of the transmission [6] and electric brake force distribution (EBD) [7], hence improving the safety and stability during the braking process. Moreover, the detection of emergency braking is of great importance to driver safety and advanced control techniques, including antilock braking system (ABS), autonomous emergency braking (AEB), and electronic stability program (ESP), which can be triggered in a timely manner [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

et al. 2019

View full text Add to dashboard Cite

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

show abstract

Section: Hybrid Machine Learning Frameworkmentioning

confidence: 99%