A Graded Offline Evaluation Framework for Intelligent Vehicle's Cognitive Ability

Cognitive ability evaluation in intelligent vehicles is conventionally evaluated by classical autonomous driving dataset, which lacks comprehensive annotations of driving difficulty. Realistically, different driving conditions require vast different level of cognitive ability, e.g., driving in highly congested traffic is much more challenging than driving on limited access highway; driving in a blizzard/hurricane requires much more robust environmental cognition abilities than driving under ordinary conditions. Different datasets contain different proportions of various driving conditions, rendering intelligent vehicle evaluation susceptible to dataset variations. To overcome such limitations, we propose to first benchmark the driving difficulty with the proposed 'Cascaded Tanks Model' and obtain a fine-grained per-segment difficulty rating based on our proposed Semantic Descriptor. With the proposed Graded Offline Evaluation (GOE) framework, it is demonstrated that offline validation of the cognitive abilities in Intelligent Vehicles (IV) is more consistent regardless of dataset choice.