Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning

2025-12-14

AI Summary

To address the high computational cost and poor real-time performance of future probabilistic occupancy grid (POG) prediction in multi-agent autonomous driving scenarios, this paper proposes a lightweight, data-driven paradigm. We design an enhanced grid representation to encode local agent interactions and prediction uncertainty, and—novelly for POG generation—introduce random forests integrated with hypothesis-based behavioral modeling and Monte Carlo trajectory sampling. Unlike conventional model-based approaches, our method avoids strong reliance on hand-crafted behavioral priors. In simulation, it achieves millisecond-level POG inference while matching the accuracy of state-of-the-art model-based methods. Experiments demonstrate significant reduction in computational load, enabling real-time criticality assessment and safety-aware trajectory planning. The proposed framework establishes a new pathway toward lightweight perception-prediction coupling in highly dynamic traffic environments.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and the prediction is taking into account various hypotheses about the behavior of traffic participants. This way, the uncertainties regarding the behavior of traffic participants can be modelled in detail. In the first part of this paper a model-based approach is presented to compute Predicted-Occupancy Grids (POG), which are introduced as a grid-based probabilistic representation of the future scenario hypotheses. However, due to the large number of possible trajectories for each traffic participant, the model-based approach comes with a very high computational load. Thus, a machine-learning approach is adopted for the computation of POGs. This work uses a novel grid-based representation of the current state of the traffic scenario and performs the mapping to POGs. This representation consists of augmented cells in an occupancy grid. The adopted machine-learning approach is based on the Random Forest algorithm. Simulations of traffic scenarios are performed to compare the machine-learning with the model-based approach. The results are promising and could enable the real-time computation of POGs for vehicle safety applications. With this detailed modelling of uncertainties, crucial components in vehicle safety systems like criticality estimation and trajectory planning can be improved.

Problem

• Predicts future traffic scenarios with multiple objects
• Estimates uncertainties in traffic participant behavior using grids
• Reduces computational load for real-time vehicle safety applications

Innovation

• Machine learning replaces model-based approach for efficiency
• Random Forest algorithm processes augmented occupancy grid cells
• Real-time computation enables improved vehicle safety applications