Uncertainty-Aware Evaluation Framework for Reliable Autonomous Driving Trajectory Forecasting
Keywords:
autonomous driving; trajectory forecasting; uncertainty quantification; evaluation frameworks; reliability engineering; socio-technical governance; robustness; fairness; deployment; policyAbstract
Autonomous driving trajectory forecasting has matured rapidly, yet evaluation practices remain disproportionately centered on average predictive accuracy. This orientation is insufficient for safety-critical deployment because it obscures rare but consequential failures, misrepresents model confidence, and neglects the socio-technical conditions under which forecasts become operational decisions. This paper proposes an uncertainty-aware evaluation framework for reliable autonomous driving trajectory forecasting. The framework treats uncertainty as a multi-layered property spanning data, representation, model, interaction, infrastructure, and governance. It argues that evaluation must shift from isolated benchmark scoring toward lifecycle assurance that integrates calibration, distributional stress testing, long-tail coverage, runtime monitoring, interpretability, and policy accountability. The paper develops a system-level architecture linking data curation, uncertainty decomposition, scenario-based validation, deployment telemetry, and regulatory audit. It examines structural trade-offs involving accuracy, robustness, latency, cost, sustainability, and fairness. It further considers how evaluation outcomes shape certification regimes, liability allocation, fleet learning, and public trust. Through cross-domain comparisons with aviation, healthcare, and power-grid assurance, the paper identifies institutional mechanisms that can make uncertainty explicit rather than hidden. The central claim is that reliable autonomous driving requires evaluation frameworks that do not merely rank models but continuously characterize when, where, and why forecasts should be trusted. Such frameworks must be technically rigorous, infrastructurally sustainable, and governable across public and private boundaries.
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