autonomous_vehicle_trajectory_transformer

Overview

This model is a sequence-to-sequence Transformer designed to predict the future paths of road agents (vehicles, cyclists, and pedestrians). It processes historical multi-sensor fusion logs to generate multi-modal trajectory predictions, allowing autonomous systems to make safer motion planning decisions in complex urban environments.

Model Architecture

The architecture consists of a Temporal-Spatial Transformer encoder-decoder.

• Input: A 2-second history of coordinates, velocities, and headings ($x, y, v, \theta$).
• Encoder: Captures temporal dependencies and social interactions between multiple agents using self-attention.
• Decoder: Employs query-based decoding to produce the next 5 seconds of coordinates at 10Hz.
• Optimization: Minimized using the Average Displacement Error (ADE) and Final Displacement Error (FDE): $$ADE=\frac{1}{T}\sum _{t=1}^T\sqrt{({\widehat{x}}_t-x_t{)}^2+({\widehat{y}}_t-y_t{)}^2}ADE\; =\; \backslash frac\{1\}\{T\}\; \backslash sum\_\{t=1\}^\{T\}\; \backslash sqrt\{(\backslash hat\{x\}\_t\; -\; x\_t)^2\; +\; (\backslash hat\{y\}\_t\; -\; y\_t)^2\}$$

Intended Use

• Collision Avoidance: Providing high-fidelity "what-if" scenarios for reactive braking and steering systems.
• Behavior Prediction: Classifying agent intent (e.g., lane change vs. straight driving) based on trajectory curvature.
• Simulation: Generating realistic agent movements for virtual testing of self-driving software stacks.

Limitations

• Occlusions: If an agent is hidden from sensors for more than 500ms, the model's uncertainty increases drastically.
• Unseen Environments: Performance may degrade in regions with traffic rules significantly different from the training data (e.g., changing from right-hand to left-hand traffic).
• Physical Constraints: Without a post-processing kinematic filter, the model might occasionally predict physically impossible movements (e.g., instantaneous 90-degree turns at high speed).