Learn about autonomous driving trajectory prediction technology in one article

Difficulty in long-term prediction: In long-term prediction (at least 3 seconds), the accumulation of small errors in the early stage may lead to large deviations from the true trajectory, reducing the prediction accuracy.

Adaptability to complex environments: In complex dynamic environments such as intersections, roundabouts, and busy urban areas, the model needs to handle complex interactions between multiple subjects, which increases the difficulty of model design.

Generalization and model limitations: The model must maintain accurate predictions in unseen driving scenarios and vehicle interactions. Although physics-based models perform well in simple scenarios and short-term predictions, they lack the ability to describe complex interactions and strategy diversity, and have difficulty coping with dynamic changes, which limits their predictions in long-term and complex environments.

Robustness: Data noise and uncertainty are also major problems. The inherent tracking errors and positioning deviations in real perception data require the algorithm to be robust and able to effectively handle imperfect information to ensure the reliability of the prediction.

In terms of future research directions, the priority is to enhance the interactive perception and environmental adaptability of the model. By combining deep learning technologies such as graph neural networks (GNNs) and attention mechanisms, we can capture complex interactive features at a deeper level, while taking into account road information and traffic rules to achieve more realistic predictions. The fusion of high-precision maps and vector maps will be the key to improving prediction accuracy, using map information to more accurately reflect road structures, guide predictions, and reduce error rates, especially for long-term predictions.

Secondly, multi-model fusion and hybrid methods are the trend, combining the immediacy of physical models with the generalization ability of learning models, such as policy optimization based on reinforcement learning, to achieve smarter predictions. Online learning and adaptability enable the model to learn new data during operation, adapt to changes, and improve the long-term accuracy of generalization and prediction. At the same time, computing efficiency optimization, lightweight, model compression and hardware acceleration strategies ensure the real-time performance of the algorithm in the vehicle.

Finally, the establishment of a standard evaluation system and data set is crucial. Building a diverse, realistic, and complex benchmark data set, and standardizing evaluation indicators, including accurate metrics for multimodal predictions, will facilitate fair comparisons and advance validation models, and promote technological development. Taking uncertainty metrics into account, such as probability assessment, to reflect the model's credibility in predictions, will be a focus of future research.

References

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