Trajectory prediction has consistently been a key focus in the realm of autonomous driving, enabling self-driving vehicles to plan ahead. In simple terms, trajectory prediction involves the autonomous driving system forecasting the potential routes, positions, speeds, and directions of moving objects on the road (such as other vehicles, pedestrians, bicycles, motorcycles, etc.) over a forthcoming period.

Compared to merely knowing the current positions and directions of these objects, trajectory prediction adds a layer of future anticipation. It not only identifies "where they are now" but also estimates "where they might head next" and "how they might behave." This plays a pivotal role between perception (observing the surroundings) and planning and control (determining the vehicle's next move) within the autonomous driving system.

Trajectory prediction goes beyond simply identifying or detecting other vehicles/pedestrians; it involves inferring the future. This mirrors how human drivers make judgments. When you drive, you don't just look for cars ahead but also assess whether a car might suddenly change lanes, accelerate, decelerate, or turn. Autonomous driving transforms this judgment into algorithms, enabling machines to anticipate potential situations in advance.

Why Does the Autonomous Driving System Require "Trajectory Prediction"?

Real-world road conditions are constantly evolving. Other vehicles may suddenly change lanes, pedestrians may cross the road, and cyclists or pedestrians may abruptly accelerate or decelerate. Safe driving necessitates more than just knowing "where they are now and how fast they are moving." If the system merely reacts passively, issues can arise, particularly at high speeds or on complex roads. Without prediction, the vehicle would simply proceed forward, which is extremely hazardous.

The role of trajectory prediction is to provide the autonomous driving system with a preliminary anticipation of the future actions of surrounding traffic participants. This enables the autonomous driving system to consider these uncertainties before making path planning and control decisions, maintaining safe distances, adjusting speeds, and selecting appropriate strategies.

For instance, when another vehicle suddenly changes lanes into the autonomous vehicle's path, the autonomous driving system can utilize trajectory prediction to determine whether to decelerate in advance or change lanes to avoid an accident. Alternatively, if a pedestrian might step out from the side of the road, the autonomous driving system can anticipate the crossing and prepare to brake or maneuver in advance.

In essence, trajectory prediction enhances the "foresight" and "proactive safety" capabilities of autonomous driving. The perception module is responsible for observing what is happening now or has just occurred; the prediction module is tasked with envisioning what might happen in the future and then conveying this vision to the decision-making/planning module, enabling the vehicle to make safe and reasonable actions in advance.

How Is Trajectory Prediction Accomplished?

Trajectory prediction involves converting "what is observed now" into "reasonable guesses about the next few seconds." To achieve this, models rely on three types of critical information inputs, generate different forms of prediction results, and employ various methods, each with its own advantages and disadvantages.

1) What Are the Inputs?

Static Environment/Map Information: This encompasses lane lines, intersections, lane shapes, road structures, no-entry zones, turning areas, traffic lights, and traffic signs. It also includes descriptions of road spatial structures in high-definition maps (HD maps) or simplified versions.

Current and Past States of Dynamic Objects: This refers to the current positions, speeds, headings, and past trajectories/motion histories of surrounding vehicles, pedestrians, etc. The past movements and speed directions of objects are crucial for predicting their future actions.

Interaction Relationships Among Traffic Participants: The mutual influences between different vehicles, pedestrians, and their surroundings. A vehicle's behavior may be affected by nearby cars, the car ahead, the car behind, as well as road signs, traffic lights, pedestrians, and cyclists. To achieve better predictions, these interactions are also considered as input features.

2) What Are the Outputs?

The output of trajectory prediction is generally the trajectory over a future period (usually a few seconds to 5-6 seconds, depending on system design), including the potential positions, speeds, and directions of the object at each future moment. It may also encompass multiple possibilities.

Since the future is uncertain, an object may exhibit several behaviors (going straight, changing lanes, decelerating, braking sharply, turning, etc.). Therefore, the prediction results are not a single trajectory but multiple possible trajectories along with the probabilities/confidence levels of each possibility (i.e., multimodal prediction).

Sometimes, the autonomous driving system only needs to know the approximate future endpoint and when it will be reached, but other times it requires a complete time-series trajectory. Multimodal outputs with probabilities enable the subsequent planning module to make more prudent decisions when faced with multiple possibilities.

3) Common Technical Approaches/Methods

In the early days, trajectory prediction could be achieved using relatively simple physical models + kinematic/dynamic models + assumption-based methods. These methods predicted short-term trajectories based on physical motion models by assuming information such as the vehicle's current speed, acceleration, and power limitations. However, this approach had poor adaptability to complex scenarios (lane changes, braking, following, group interactions, pedestrian crossings, etc.).

In recent years, data-driven (data-driven) or machine learning/deep learning methods have become more prevalent. Autonomous driving systems train models using vast amounts of real-world traffic data, taking historical trajectories + environmental information as inputs, and allowing the models to learn behavior patterns in similar situations to predict future trajectories.

Some models treat all nearby vehicles/pedestrians as "nodes" and construct graphs to represent their potential interaction relationships (who might influence whom). Then, using graph neural networks (graph neural network) + encoder-decoder (encoder-decoder) / recurrent neural networks (RNN/LSTM) / Transformer structures, they predict future trajectories. Other models incorporate static information such as road structures, lane lines, traffic rules, and environmental semantics to make predictions more rule-compliant.

Trajectory prediction in autonomous driving systems is a complex process that integrates perception, learning, inference, and environmental constraints, rather than simply extrapolating based on current speed and direction.

The Position and Role of the Trajectory Prediction Module in the Autonomous Driving Architecture

The autonomous driving system can be divided into several modules: perception → prediction → planning/decision-making → control/actuation. Trajectory prediction is situated between perception and planning, serving as a crucial bridge connecting the two.

The perception module is responsible for identifying the surrounding static environment (roads, lane lines, buildings, pedestrians, traffic signs) and dynamic objects (other vehicles, pedestrians, bicycles, etc.), informing the system "what is present now, where they are, how fast they are moving, and which direction they are heading."

The prediction module takes over this information and estimates the potential future trajectories of each dynamic object, predicting their trajectories, behavioral intentions (such as lane changes, deceleration, turning, U-turns, stopping, crossing the road, etc.), and outputting the possible positions/speeds/trajectory distributions of each object over the next few seconds.

The planning/decision-making module receives these prediction results and considers how its own vehicle should proceed: whether to decelerate, change lanes, brake, maneuver, stop, yield, etc., and generates decision-making/driving plans for the vehicle.

The control module executes specific controls through steering, acceleration/deceleration, braking, and other actions based on the planning results.

Without the prediction module, even with highly accurate perception, the vehicle can only react passively, responding based on what it sees now. This approach may suffice in simple scenarios or at low speeds but can easily lead to delayed judgments, slow reactions, inadequate avoidance, harsh or abrupt braking, and other issues in high-speed, complex, multi-target, and variable scenarios (urban roads, intersections, highways, pedestrian-dense areas, mixed traffic), severely affecting safety and comfort.

Therefore, trajectory prediction serves as the "safety anticipation mechanism" of the autonomous driving system, enabling the vehicle to anticipate potential events in advance and reserve space/time/strategies, providing more reliable inputs for subsequent planning and control.

Limitations of Trajectory Prediction

Trajectory prediction is crucial for autonomous driving, but achieving accurate, reliable, and real-time predictions faces numerous challenges.

1) Complex Multi-Agent/Multi-Object Interactions

Roads are teeming with various traffic participants, including cars, bicycles, pedestrians, motorcycles, etc., who interact with each other. One person's action may influence another vehicle's behavior, pedestrians may interact with vehicles, and bicycles may suddenly merge lanes. This multi-agent interaction is complex and difficult to model. A simple straight-line prediction is clearly insufficient. Data-driven models attempt to capture these relationships through graph models/neural networks, but ensuring applicability to all complex scenarios remains extremely challenging.

2) Behavioral Diversity/Uncertainty (Multimodal Problem)

The same traffic participant may perform entirely different actions at different times or in different contexts. For example, a vehicle may continue straight in its current lane, change lanes, decelerate, or accelerate. This means there are multiple possible future trajectories (multiple possible futures) rather than a single, deterministic one. How the prediction system simultaneously provides these possibilities along with reasonable probabilities or confidence levels is a difficult problem. If the system only outputs a single trajectory and the actual object follows a different path, it could lead to collisions or dangers.

3) Difficulty Integrating Static Environment and Rule Constraints

Road structures, lane lines, traffic rules (who has priority, traffic lights, pedestrian crossings, no-entry zones, narrow roads, curves, slopes, etc.) significantly impact vehicle/pedestrian trajectories. A prediction model that ignores these constraints may produce absurd, rule-violating predictions (e.g., predicting a pedestrian to cross a guardrail/go against traffic, predicting a vehicle to pass through a building/cross lines/ignore traffic rules). Therefore, effectively integrating environmental/map/rule information into trajectory prediction is essential to ensure accuracy and driving safety.

4) Real-Time Performance and Algorithmic Complexity/Computational Resource Constraints

Autonomous driving must respond swiftly to real-time environments. The prediction module cannot be too slow; otherwise, the generated trajectories may become outdated. However, achieving real-time prediction with highly complex models (deep networks + multi-agent interactions + multi-possibility calculations + map fusion) requires significant computational resources. If real-time performance is insufficient, computational resources are lacking, or latency is too high, deployment becomes impractical. Finding a balance between pursuing ultimate prediction accuracy and overall system response speed is crucial.

5) Discrepancies Between Evaluation and Real-World Scenarios ("Dataset vs. Actual Driving Environment")

Many trajectory prediction techniques are trained/tested on fixed datasets/past records, assuming that all object behaviors will follow historical trajectories. However, in real traffic scenarios, the autonomous vehicle itself can influence the behavior of surrounding people/vehicles due to its prediction results/decisions/actions. In other words, the real world is interactive. A model's high accuracy on a static dataset does not necessarily guarantee good performance on real roads. This discrepancy (dynamics gap) is a critical issue that must be addressed when applying trajectory prediction to autonomous driving.

Final Thoughts

As autonomous driving systems evolve from advanced driver-assistance systems (ADAS) to higher levels (such as L3/L4/L5), the demand for safe, reliable, and comprehensive perception continues to grow. In complex urban traffic and mixed traffic scenarios (cars + bicycles + pedestrians + electric vehicles + motorcycles + pedestrians), simply seeing and reacting is far from sufficient. Autonomous driving systems must possess anticipatory capabilities, knowing what others might do and preparing response plans for various situations in advance. Trajectory prediction provides autonomous driving systems with a pair of eyes/a brain that can foresee the future.

Without trajectory prediction, autonomous driving can only perceive the present and react, leading to delays, inability to brake in time, inadequate avoidance, and misjudgments. With trajectory prediction, it becomes safer, smoother, and more human-like in driving. Trajectory prediction represents a significant step toward truly robust, safe, and autonomous autonomous driving systems.

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