How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction

By Nachiket Deo, Akshay Rangesh, Mohan Manubhai Trivedi. IEEE Transactions on Intelligent Vehicles, 2018

336 citations recorded by the source corpus.

Reliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. In this paper, we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and intervehicle interaction. We report our results in terms of maneuver classification accuracy and mean and median absolute error of predicted trajectories against the ground truth for real traffic data collected using vehicle mounted sensors on freeways. An ablative analysis is performed to analyze the relative importance of each cue for trajectory prediction. Additionally, an analysis of execution time for the components of the framework is presented. Finally, we present multiple case studies analyzing the outputs of our model for complex traffic scenarios.

Subfields: Artificial Intelligence, Automotive Engineering, Building and Construction

View Paper: https://www.semanticscholar.org/paper/79e8af07d7be3e41ebdc2d1a219d5c867693bf4c

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Citations in the corpus

Multiple Futures Prediction (2019, neutral): “CVGMM[15] [23] MATF[39] LSTM S-LSTM[1] CS-LSTM(M) MFP-1 MFP-2 MFP-3 MFP-4 MFP-5”
Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems (2020, methodology): “Based on the maneuver classification realized by the HMM, the authors in [39] predicted the vehicles’ motions by VGMMs (variational GMMs) under the constraints of vehicleinteraction models.”
GRIP: Graph-based Interaction-aware Trajectory Prediction (2019, background): “Prediction Horizon (s) CV V-LSTM C-VGMM + VIM [10] GAIL-GRU [15] CS-LSTM(M) [11] CS-LSTM [11] GRIP (4CS-LSTM) GRIP (ALL) 1 0.73 0.68 0.66 0.69 0.62 0.61 0.37 (40%↑ -0.24) 0.64 2 1.78 1.65 1.56 1.51 1.29 1.27 0.86 (32%↑ -0.41) 1.13 3 3.13 2.91 2.75 2.55 2.13 2.09 1.45 (31%↑ -0.64) 1.80 4 4.78 4.46 4.”
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction (2019, methodology): “Our deterministic model MATF Multi Agent outperforms the state-of-the-art deterministic model C-VGMM + VIM [8], a recent vehicle interaction approach based on variational Gaussian mixture models with Markov random ﬁelds.”
A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “Deo et al. [55] propose a vehicle trajectory prediction model based on HMM and Variational Gaussian Mixture Models (GMM) considering interaction-related factors.”
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.”
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs (2018, methodology): “Of particular interest are works that use the recognized maneuvers to make better predictions of future trajectories [10], [11], [13]–[16].”