This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Situational Intelligence-Based Vehicle Trajectory Prediction in an Unstructured Off-Road Environment
Technical Paper
2023-01-0860
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Autonomous vehicles (AV) are sophisticated systems comprising various sensors, powerful processors, and complex data processing algorithms that navigate autonomously to their respective goals. Out of several functions performed by an AV, one of the most important is developing situational intelligence to predict collision-free future trajectories. As an AV operates in environments consisting of various entities, such as other AVs, human-driven vehicles, and static obstacles, developing situational intelligence will require a collaborative approach. The recent developments in artificial intelligence (AI) and deep learning (DL) relating to AVs have shown that DL-based models can take advantage of information sharing and collaboration to develop such intelligence. However, most of these developments address only the requirements of urban environments, which are structured, and ignore the more challenging requirements of off-road environments, which are unstructured due to the lack of lane markings, traffic rules, and traffic signs. Given this deficiency and the lack of off-road vehicle motion and interaction data, we first employ two groups of AVs that will navigate in an unstructured environment to generate an off-road vehicle dataset. Based on the dataset, an encoder-decoder social long short-term memory (LSTM) network is developed to function as a situational intelligence model. The model comprises two data streams - a social interaction stream that learns how AVs interacts with their surroundings and a vehicle behavior stream that learns the dynamic characteristics of AVs. The model’s output is a multi-modal Gaussian distribution of all collision-free future AV trajectories. Finally, the effectiveness of the model is tested and verified using an independent subset of the dataset.
DISTRIBUTION A. Approved for public release; distribution unlimited. OPSEC#6871 (Pending, NOT approved for release).
Citation
Prasanna Kumar, R. and Jia, Y., "Situational Intelligence-Based Vehicle Trajectory Prediction in an Unstructured Off-Road Environment," SAE Technical Paper 2023-01-0860, 2023, https://doi.org/10.4271/2023-01-0860.Also In
References
- Mozaffari , S. , Al-Jarrah , O.Y. , Dianati , M. , Jennings , P. et al. Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review IEEE Transactions on Intelligent Transportation Systems 23 1 2020 33 47 10.1109/TITS.2020.3012034
- Lefèvre , S. , Vasquez , D. , and Laugier , C. A Survey on Motion Prediction and Risk Assessment for Intelligent Vehicles ROBOMECH journal 1 1 2014 1 14
- Deo , N. , and Trivedi M.M. Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver-Based LSTM 2018 IEEE Intelligent Vehicles Symposium (IV) 1179 1184 IEEE 2018 10.1109/IVS.2018.8500493
- Holder , C.J. , and Breckon T.P. Learning to Drive: Using Visual Odometry to Bootstrap Deep Learning for off-Road Path Prediction 2018 IEEE Intelligent Vehicles Symposium (IV) 2104 2110 IEEE 2018
- Alon , Y. , Ferencz A. , and Shashua A. Off-Road Path Following Using Region Classification and Geometric Projection Constraints 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) 1 689 696 IEEE 2006
- Alahi , A. , Goel K. , Ramanathan V. , Robicquet A. , et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces Proceedings of the IEEE conference on computer vision and pattern recognition 961 971 2016
- Ding , W. , Chen J. , and Shen S. Predicting Vehicle Behaviors over an Extended Horizon Using Behavior Interaction Network 2019 International Conference on Robotics and Automation (ICRA) 8634 8640 IEEE 2019 10.1109/ICRA.2019.8794146
- Phillips , D.J. , Wheeler T.A. , and Kochenderfer M.J. Generalizable Intention Prediction of Human Drivers at Intersections 2017 IEEE intelligent vehicles symposium (IV) 1665 1670 IEEE 2017 10.1109/IVS.2017.7995948
- Dai , S. , Li , L. , and Li , Z. Modeling Vehicle Interactions Via Modified LSTM Models for Trajectory Prediction IEEE Access 7 2019 38287 38296 10.1109/ACCESS.2019.2907000
- Lee , D. , Kwon Y.P. , McMains S. , and Hedrick J.K. Convolution Neural Network-Based Lane Change Intention Prediction of Surrounding Vehicles for ACC 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) 1 6 IEEE 2017 10.1109/ITSC.2017.8317874
- Li , X. , Ying X. , and Chuah M.C. Grip: Graph-Based Interaction-Aware Trajectory Prediction 2019 IEEE Intelligent Transportation Systems Conference (ITSC) 3960 3966 IEEE 2019 10.1109/ITSC.2019.8917228
- Luo , W. , Yang B. , and Urtasun R. Fast and Furious: Real Time End-to-End 3d Detection, Tracking and Motion Forecasting with a Single Convolutional Net Proceedings of the IEEE conference on Computer Vision and Pattern Recognition 3569 3577 2018
- Casas , S. , Luo W. , and Urtasun R. Intentnet: Learning to Predict Intention from Raw Sensor Data Conference on Robot Learning 947 956 PMLR 2018
- Dutra , T.B. , Marques R. , Cavalcante-Neto J.B. , Vidal C.A. , et al. Gradient-Based Steering for Vision-Based Crowd Simulation Algorithms Computer Graphics Forum 36 2 337 348 2017
- Deo , N. , and Trivedi M.M. Convolutional Social Pooling for Vehicle Trajectory Prediction Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 1468 1476 2018
- Marchetti , F. n.d. Marchetz/KITTI-Trajectory-Prediction https://github.com/Marchetz/KITTI-trajectory-prediction
- Perception https://waymo.com/open/data/perception/
- Prediction https://level-5.global/data/prediction/