Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Gaussian Lane Keeping: A Robust Prediction Baseline

David Isele, Piyush Gupta, Xinyi Liu, Sangjae Bae

TL;DR

This work tackles the need for robust, interpretable trajectory prediction baselines for autonomous driving. It introduces Gaussian Lane Keeping (GLK), a Bayesian fusion of Constant Velocity and Lane Snapping predictions that yields a Gaussian at each step with mean and covariance computed from $\mu_{GLK,t}$ and $\Sigma_{GLK,t}$, respectively. The authors extend GLK with curvature, multi-modal, and interactive (IDM-based) predictions, and evaluate on CitySim, showing that GLK variants can outperform a state-of-the-art neural model (GATraj) while maintaining simplicity and training-free operation. They also provide qualitative analyses, ablations, and a real-vehicle demonstration, arguing for GLK as a practical, robust baseline to benchmark new prediction approaches and facilitate debugging in real-world deployments.

Abstract

Predicting agents' behavior for vehicles and pedestrians is challenging due to a myriad of factors including the uncertainty attached to different intentions, inter-agent interactions, traffic (environment) rules, individual inclinations, and agent dynamics. Consequently, a plethora of neural network-driven prediction models have been introduced in the literature to encompass these intricacies to accurately predict the agent behavior. Nevertheless, many of these approaches falter when confronted with scenarios beyond their training datasets, and lack interpretability, raising concerns about their suitability for real-world applications such as autonomous driving. Moreover, these models frequently demand additional training, substantial computational resources, or specific input features necessitating extensive implementation endeavors. In response, we propose Gaussian Lane Keeping (GLK), a robust prediction method for autonomous vehicles that can provide a solid baseline for comparison when developing new algorithms and a sanity check for real-world deployment. We provide several extensions to the GLK model, evaluate it on the CitySim dataset, and show that it outperforms the neural-network based predictions.

Gaussian Lane Keeping: A Robust Prediction Baseline

TL;DR

This work tackles the need for robust, interpretable trajectory prediction baselines for autonomous driving. It introduces Gaussian Lane Keeping (GLK), a Bayesian fusion of Constant Velocity and Lane Snapping predictions that yields a Gaussian at each step with mean and covariance computed from and , respectively. The authors extend GLK with curvature, multi-modal, and interactive (IDM-based) predictions, and evaluate on CitySim, showing that GLK variants can outperform a state-of-the-art neural model (GATraj) while maintaining simplicity and training-free operation. They also provide qualitative analyses, ablations, and a real-vehicle demonstration, arguing for GLK as a practical, robust baseline to benchmark new prediction approaches and facilitate debugging in real-world deployments.

Abstract

Predicting agents' behavior for vehicles and pedestrians is challenging due to a myriad of factors including the uncertainty attached to different intentions, inter-agent interactions, traffic (environment) rules, individual inclinations, and agent dynamics. Consequently, a plethora of neural network-driven prediction models have been introduced in the literature to encompass these intricacies to accurately predict the agent behavior. Nevertheless, many of these approaches falter when confronted with scenarios beyond their training datasets, and lack interpretability, raising concerns about their suitability for real-world applications such as autonomous driving. Moreover, these models frequently demand additional training, substantial computational resources, or specific input features necessitating extensive implementation endeavors. In response, we propose Gaussian Lane Keeping (GLK), a robust prediction method for autonomous vehicles that can provide a solid baseline for comparison when developing new algorithms and a sanity check for real-world deployment. We provide several extensions to the GLK model, evaluate it on the CitySim dataset, and show that it outperforms the neural-network based predictions.
Paper Structure (18 sections, 1 theorem, 12 equations, 9 figures, 2 tables, 1 algorithm)

This paper contains 18 sections, 1 theorem, 12 equations, 9 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Constant Velocity (CV) prediction as a baseline
  4. Lane Snapping with Constant Velocity (LS-CV)
  5. Intelligent Driver Model (IDM)
  6. Gaussian Lane Keeping
  7. Extensions
  8. Curvature
  9. Multi-modal Predictions
  10. Interactive Predictions
  11. Metrics
  12. Evaluation
  13. Comparison with Deep Learning Method
  14. Baseline Model Evaluations
  15. Qualitative analysis
  16. ...and 3 more sections

Key Result

Theorem 1

Given the mean $\mu_{t-1}$ and the variance $\Sigma_{t-1}$ of the Gaussian prediction at $t-1$, i.e, $\mathbb{P}(X_{t-1}) = \mathcal{N}(\mu_{t-1}, \Sigma_{t-1})$, the GLK prediction at time $t$ is given by $\mathcal{N}(\mu_{GLK,t}, \Sigma_{GLK,t})$, where $\mu_{GLK,t}=(I-K)A\mu_{t-1} + Kg(\mu_{t-1})

Figures (9)

  • Figure 1: Accelerations of various traffic agents from an arbitrary time window in the CitySim dataset. Each color represents a different agent. It's evident that the accelerations are noisy and do not exhibit a constant profile.
  • Figure 2: Lane Snapping prediction by locally approximating lane center as a line. The blue vehicle shows the vehicle position and heading at time $t-1$. The green vehicle shows the lane projection of the vehicle position at time $t-1$ and the red vehicle shows the lane snapping prediction at time $t$.
  • Figure 3: Visualization of GLK. CV predictions (cyan) indicate the car moving forward with a constant heading. LS-CV model (gray) assumes the car stays in the lane. The GLK prediction (magenta) shows the resulting prediction formed from the combination of CV (dark blue) and LS-CV (green) components at each timestep.
  • Figure 4: Intersection B scenario from the CitySim Dataset
  • Figure 5: Sorted Errors for GATraj on CitySim (out of distribution)
  • ...and 4 more figures

Theorems & Definitions (3)

  • Remark 1
  • Theorem 1
  • proof