Author manuscript, published in "IEEE Intelligent Vehicles Symposium (2013)" Learning-Based Approach for Online Lane Change Intention Prediction

2013

Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “3 seconds in advance with support vector machines (SVM) and Bayesian filters [214].”
Predicting the Driver's Focus of Attention: The DR(eye)VE Project (2017, background): “Among ADAS solutions, the most ambitious examples are related to monitoring systems [21], [29], [33], [43]: they parse the attention behavior of the driver together with the road scene to predict potentially unsafe manoeuvres and act on the car in order to avoid them—either by signaling the driver o”
A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving (2017, methodology): “Vehicle kinematics, Relations with road entities no TTx [12,16] HMM [12,16] SVM-BF [12,20] Vehicle kinematics, Relations with road entities, Interactions with other vehicles no BN [11] Layered-HMM [15] Vehicle kinematics, Relations with road entities, Interactions with other vehicles, A priori…”
An LSTM network for highway trajectory prediction (2017, methodology): “Many techniques have already been explored for behavior prediction, such as hidden Markov models [3], [4], Kalman ﬁltering [5], Support Vector Machines [6], [7] or directly using a vehicle model [8]; more recently, artiﬁcial neural network approaches have also been proposed [9]–[11].”
A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “Since SVM can output the characteristics of classiﬁcation probability, Kumar et al. [47] propose a layered architecture method combining SVM and Bayesian ﬁltering to identify lane-changing maneuvers so as to obtain more accurate identiﬁcation results.”
A survey on motion prediction and risk assessment for intelligent vehicles (2014, unknown): “[[43]] Lane keeping, lane change left, SVM-BF Distance to lane center, steering angle”
Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks (2018, methodology): “There have been several approaches to this problem recently, with the use of Support Vector Machines [3], Hidden Markov Models [4], and Bayesian Networks [5].”