Multi-Agent Trajectory Prediction in Autonomous Driving

Trajectory prediction is one of the fundamental and most complex tasks in the autonomous driving system. It involves calculating the probabilistic distribution of the future positions of all dynamic road users over a specific time horizon.

The task of prediction goes beyond simple extrapolation of speed and direction because the movement of objects is determined by complex intentions and social interactions, not just physical inertia.

In the real world, movement is interdependent: the decision of one participant directly influences the possible actions of another. The prediction results are an important input vector for the planning module. The planner can choose trajectories that are guaranteed to avoid collisions with all the most probable future positions of other objects.

The autonomous vehicle can behave efficiently and "socially," integrating into the general traffic flow instead of acting in isolation. Thus, the quality of prediction directly determines the reliability, safety, and comfort of the entire autonomous driving system's operation.

Quick Take

• An agent is any dynamic object whose behavior needs to be predicted.
• Multi-Modal trajectories are the standard: a quality model generates a set of probable paths.
• Graph Neural Networks are a key tool for processing interaction, representing the scene as a graph where nodes are agents and edges are their relationships.
• Models must be accurate enough to predict complex behavior but fast enough for the vehicle to react.
• Quality assessment includes not only position error but also scenario coverage – whether the agent's actual path falls within one of the predicted options.

Scene Elements and Their Prediction

To ensure safety, autonomous driving systems must not only see but also anticipate the actions of all road users.

Who Are the Agents in Autonomous Driving

An agent is any dynamic object capable of moving and influencing the autonomous vehicle's trajectory. These are all road users whose behavior needs to be predicted. The main agents include cars, pedestrians, cyclists, and motorcyclists. Sometimes, even large animals or dense groups of people are included in this category.

Each agent has its own goals and constraints. A pedestrian can suddenly change direction, while a car is more predictable. Behavior forecasting models must take into account the movement style of each agent.

Data Sources for Trajectory Prediction

The quality of predicting the future path directly depends on the quality and completeness of the input data.

• Vehicle Sensors. The main sources are LiDAR, cameras, and radar, which provide information about the agent's current position, speed, and acceleration. This data forms the history of object movement.
• High-Definition Maps. Maps provide static but critically important scene context: lane markings, road boundaries, and pedestrian crossing zones. Motion forecasting uses this context to understand where an object can go and where it cannot.
• Scene Context. Additional data, such as traffic light signals, road signs, or time of day, helps models more accurately understand the agent's intention.

The Essence of the Multi-Agent Approach

This is the most complex part that distinguishes simple prediction from true multi-modal trajectories.

Not only physical laws but also social rules act on the road. Drivers yield the right-of-way, anticipate another car's braking, or avoid dangerous "cutting in." Models must be able to simulate this interaction.

Prediction models must account for the fact that the movement of agent A affects the movement of agent B. For example, if our autonomous vehicle accelerates, a pedestrian on the curb might decide to wait rather than cross the road.

By accounting for the interaction between agents, autonomous driving systems can generate safe, realistic, and smooth predictions. This multi-agent approach ensures that the autopilot's planner will not encounter unexpected reactions from other road users.

Architecture and Prediction Execution

To successfully solve the future path prediction problem, engineers use complex models that must operate very quickly and account for human unpredictability.

Main Types of Prediction Models

Modern systems rarely rely on a single method, combining several approaches for better stability and accuracy.

• Classical Rule-Based Approaches. These are the simplest models that use elementary rules or physical models. They are fast but completely unable to predict social interaction or unexpected maneuvers.
• Neural Networks and Deep Learning. Modern solutions are predominantly based on deep learning. Neural networks are trained on huge volumes of real movement data to recognize complex behavioral patterns.
• Graph-Based Models. This is the key tool for solving the multi-agent problem. They represent the road scene as a graph, where cars and pedestrians are nodes. GNNs allow models to effectively process the interaction between all objects simultaneously, for example, how one car's speed change affects another's decision.
• Attention Mechanisms. These mechanisms allow the model to focus on the most important agents or scene elements. For example, if there are many cars at an intersection, the model will pay more "attention" to an object that is closer and may pose a danger.

Uncertainty and Multiple Possible Scenarios

The main difference in quality behavior forecasting is that it predicts not one trajectory, but several probable options. Human behavior is probabilistic. For example, a driver approaching an intersection may turn left, go straight, or stop to wait. All these options have a certain probability.

Thus, the model generates a set of the most probable multimodal trajectories for each object. Having multiple scenarios is critical for safety. The autonomous vehicle's planning module must choose a maneuver that is safe with respect to all probable future paths of other objects, even unlikely ones, to prevent a collision.

Real-Time and Performance Constraints

Prediction must work extremely fast because it directly affects the vehicle's decision-making ability. Decisions in an autonomous vehicle are made in milliseconds. If the prediction takes too long, the vehicle loses precious reaction time to danger, making it unsafe.

Models operate on the vehicle's onboard equipment, which has limited computational power. This affects the models' architecture and requires their maximal optimization.

A balance must be found between prediction accuracy and execution speed. The model must be complex enough to predict interaction but fast enough to work in real time.

Impact on Decisions and the Future of Autonomous Driving

To ensure the safety and efficiency of autonomous systems, it is not enough to have a fast model; it is necessary to constantly evaluate its accuracy and ensure its seamless integration with the decision-making module.

Prediction Quality Assessment

Evaluating the quality of a motion forecasting model is a multifaceted process that goes beyond simple accuracy.

• Trajectory Deviation. The main metrics are the average displacement error and the final displacement error. They measure the average distance between the predicted point and the agent's actual position at the end of the predicted period. The smaller the deviation, the better the accuracy.
• Scenario Coverage. Since models generate several possible scenarios, it is important to assess whether the agent's actual trajectory falls within one of the proposed probabilistic options. This indicates the model's reliability.
• Stability in Complex Situations. A quality model must maintain low error in difficult traffic conditions, at intersections, in heavy traffic, or during interaction with unpredictable pedestrians.

Integration with Motion Planning

Trajectory prediction is a direct input for the autonomous vehicle's decision-making module. The prediction results are a set of probabilistic future paths for all agents. The planning module uses this data to calculate a safe trajectory for the autonomous vehicle that minimizes the risk of collision with any of the predicted scenarios.

A prediction error can lead to dangerous decisions. For example, if the model incorrectly predicts that a car will go straight, but it turns, the autopilot may not have time to brake. Or, if the model is too pessimistic, the car may move too slowly or aggressively, which reduces comfort.

Practical Applications and Future Directions

Prediction technology has already moved beyond research labs, and its impact continues to grow. Accurate prediction models are critical for ADAS, robotaxis, autonomous delivery, and logistics management systems.

The industry is moving toward generating a greater number of scenarios to cover rare but dangerous situations. In addition, there is growing interest in the interpretability of models – understanding why the model made a specific prediction and not another. This allows for improved reliability and safety. Closer integration with simulations for faster testing and validation is also being explored.

FAQ

Why do prediction models often "hallucinate" or give unrealistic results?

Models can generate unrealistic trajectories if they lack full context or when training datasets contain incomplete/contradictory examples. This also happens when the model is not sufficiently constrained by physical rules.

How does the autonomous driving system determine the agent's "intention"?

"Intention" is determined indirectly based on movement history, current position relative to road markings, and speed. For example, if a car is slowly drifting toward the lane divider line before an intersection, the system assumes a high probability of a turn. This intention is the input data for prediction.

What typical computational delay is considered acceptable for the prediction module?

For safe operation in real-time, the total delay should be on the order of tens of milliseconds. Too much delay makes predictions outdated and unsuitable for planning.

Why is prediction over long time horizons practically impossible?

The uncertainty of human behavior grows exponentially with time. The further into the future, the more scenarios become possible, making the prediction too "foggy" and useless for planning. The planner can only effectively use predictions with high probability for the nearest few seconds.