Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

By Christian Laugier, Igor E. Paromtchik, Mathias Perrollaz, Yongxing Mao, John-David Yoder, Christopher Tay, Kamel Mekhnacha, Amaury Nègre. IEEE Intelligent Transportation Systems Magazine, 2011

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b92249d8cfe4874efbbda764f1b771229abcdbc8

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Citations in the corpus

A survey on motion prediction and risk assessment for intelligent vehicles (2014, unknown): “(a) RRT (adapted from [[42]]) (b) GP (adapted from [[49]]) (c) Stochastic reachable sets (adapted from [[50]]).”
Vehicle trajectory prediction based on motion model and maneuver recognition (2013, background): “In [7] and [8], a set of trajectories corresponding to different maneuvers is predefined.”
A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “In [41] HMM is used to estimate likely behaviors, then GP is employed to predict the trajectories.”
Convolutional Social Pooling for Vehicle Trajectory Prediction (2018, methodology): “Heuristic based classiﬁers [8], Bayesian networks [21], hidden Markov models [6, 14], random forest classiﬁers [20] and recurrent neural networks have been used for maneuver recognition.”
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer (2019, background): “This objective risk framework led to research focusing on vehicle tracking, where motion models predicted the future position of vehicles, allowing risk assessment [11], [12].”
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs (2018, methodology): “Polynomial fitting [13], maneuver specific motion models [14], Gaussian processes [11], [15], Gaussian mixture models [10] have been used for trajectory prediction.”
How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction (2018, methodology): “We thus divide the trajectories in our training data into overlapping snippets of t h seconds and train the maneuver HMMs using these snippets.”
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice (2018, methodology): “In intelligent driving systems, intention estimation techniques have been widely used for predicting the behavior of the drivers [15], [124], other drivers [125], [126], pedestrians [127], [128] or combinations of any of these three [129], [130] (for a more detailed list of these techniques see [131”
A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving (2017, background): “…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 knowledge no DBN [13] no Cost-map + BF [14] Yes…”
Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network (2017, methodology): “The vehicle trajectory generating model has been learned through Gaussian process [5], [6] and Gaussian mixture model [7].”