This study presents a denominator-conditioned functional-coverage framework for rear-end automated emergency braking (AEB). The framework estimates what share of a declared modeled rear-end hazard denominator is controllable at a given intervention lead time. Ego–lead episodes come from NGSIM US-101 and I-80; the default D0 denominator is a modeled no-action counterfactual from episode-entry states, not a replay of observed future motion or a crash-rate estimator. A longitudinal brake-only replay model estimates policy-specific required lead times for transparent reference policies: a brake-only upper envelope, TTC-family references, a stopping-distance reference, and an RSS-style baseline. Under the default pipeline, the upper envelope saturates near 99%, the weak TTC anchor covers roughly half of the denominator, and stronger references reach about 95–96% frequency-weighted saturation. Duration filters change the saturation by at most 4.00 percentage points, and increasing physical-uncertainty realizations from R = 3 to R = 30 changes the saturation by at most 0.33 percentage points. An extended TTC-family audit shows that a coverage-only candidate reaches 99.00% saturation but 30.70% nuisance intervention, whereas a burden-constrained candidate reaches 97.88% saturation with 9.23% nuisance. The reported values are conditional controllability measures, not certification evidence or deployment-level crash-rate estimates.

To achieve the accurate energy estimation and improve the economy performance of Extended Range Electric Vehicle (EREV) under various information from vehicle-road-cloud systems, an energy consumption prediction and optimal control strategy for EREV is proposed based on fused average vehicle speed, road segment length, and traffic congestion information. In the dataset, the average speed of different road segments, segment length, and congestion information are selected as the three neurons in the input layer of Back Propagation (BP) neural network model. Through iterative gradient search training, the network parameters are determined, with the model output set as the energy consumption of future road segments. Real-time traffic information is obtained, and the predicted energy consumption output by the BP model is utilized to dynamically adjust the State of Charge (SOC) balance point. The operating point of the range extender engine is optimized in real time to achieve optimal energy efficiency. The Simulations results indicate that when the numbers of predicted road segments are 4, 5, and 6, the predictive strategy enables the pre-estimation of energy consumption levels for future road segments. By reasonably adjusting SOC balance point, energy replenishment is provided in high-efficiency zones of the range extender for congested segments.