mlsanityData to 14 Oct 2026

Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems

arXiv 2212.12819 ↗First posted 24 Dec 2022cs.RO cs.AIcited by 0

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Abstract (arXiv)

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

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References · 16

  1. 2018An LSTM Network for Highway Trajectory Predictioncited by 81
  2. 2021Social Coordination and Altruism in Autonomous Drivingcited by 28
  3. 2021Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Spacecited by 21
  4. 2019Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networkscited by 15
  5. 2018Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networkscited by 7
  6. 2019Real-Time Hardware-In-the-Loop Emulation Framework for DSRC-based Connected Vehicle Applicationscited by 7
  7. 2019V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structurescited by 6
  8. 2021AICP: Augmented Informative Cooperative Perceptioncited by 6
  9. 2021Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Controlcited by 6
  10. 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Drivingcited by 6
  11. 2022Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Controlcited by 5
  12. 2018Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Applicationcited by 4
4 more
  1. 2020Representing Realistic Human Driver Behaviors using a Finite Size Gaussian Process Kernel Bankcited by 4
  2. 2021Impact of Communication Loss on MPC based Cooperative Adaptive Cruise Control and Platooningcited by 4
  3. 2021Towards Learning Generalizable Driving Policies from Restricted Latent Representationscited by 4
  4. 2022Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithmscited by 3

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