TY - JOUR
T1 - Advancing Trustworthy Multi-Modal Motion Prediction for Autonomous Vehicles through Holistic Evaluation and Interpretable Outputs
AU - Sara Ben Youssef
AU - Amal Trabelsi
AU - Karim Boudiaf
AU - Nabil Jebali
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2023
VL - 2
IS - 2
SP - 36
N2 - Anticipating the movement of nearby traffic participants allows automated vehicles to formulate safe and efficient navigation strategies. This undertaking is highly intricate, given that the conduct of road users is shaped by numerous variables, resulting in a substantial variety of potential future paths (multi-modality). The majority of previous methodologies designed to tackle multi-modal trajectory forecasting rely on intricate machine learning architectures that offer minimal interpretability. Furthermore, the performance measures utilized in contemporary benchmarks fail to assess every dimension of the issue, particularly regarding the variety and validity of the generated paths.The authors intend to progress the creation of dependable trajectory forecasting systems by aligning with specific tenets established for Trustworthy Artificial Intelligence. The primary emphasis is directed toward assessment benchmarks, structural resilience, and the explainability of model outputs. Initially, existing appraisal metrics undergo a detailed examination to pinpoint the major deficiencies in current benchmarks, leading to the introduction of a new, comprehensive assessment framework. Following this, a methodology to evaluate spatial and temporal resilience is developed by replicating sensor noise within the perception pipeline. To improve output interpretability and produce more equitable outcomes across the proposed evaluation metrics, an intention forecasting layer is introduced that can be integrated into existing multi-modal trajectory models. The utility of this configuration is analyzed via a user study that investigates distinct graphical representations of multi-modal paths and driver intents. The introduced methodology and subsequent insights offer a meaningful advancement toward constructing dependable trajectory forecasting frameworks for automated vehicles, shifting the discipline toward elevated levels of safety and operational dependability.
UR - https://tipublications.com/z555426335
ER -