We describe Urban Driving Games (UDGs) as a particular class of differential games that model the interactions and incentives of the urban driving task. The drivers possess a "communal" interest, such as not colliding with each other, but are also self-interested in fulfilling traffic rules and personal objectives. Subject to their physical dynamics, the preference of the agents is expressed via a lexicographic relation that puts as first priority the shared objective of not colliding. Under mild assumptions, we show that communal UDGs have the structure of a lexicographic ordinal potential game which allows us to prove several interesting properties. Namely, socially efficient equilibria can be found by solving a single (lexicographic) optimal control problem and iterated best response schemes have desirable convergence guarantees.
Typically, to enlarge the operating domain of an object detector, more labeled training data is required. We describe a method called wormhole learning, which allows to extend the operating domain without additional data, but only with temporary access to an auxiliary sensor with certain invariance properties.We describe the instantiation of this principle with a regular visible-light RGB camera as the main sensor, and an infrared sensor as the temporary sensor. We start with a pre-trained RGB detector; then we train the infrared detector based on the RGB-inferred labels; finally we re-train the RGB detector based on the infrared-inferred labels. After these two transferlearning steps, the RGB detector has enlarged its operating domain by inheriting part of the invariance to illumination of the infrared sensor; in particular, the RGB detector is now able to see much better at night.We analyze the wormhole learning phenomenon by bounding the possible gain in accuracy using mutual information properties of the two sensors and considered operating domain.
Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at equilibrium to build a leaner game graph. When applicable, it yields fewer nodes, fewer players per game node, hence much faster solutions. While for the general case checking for independence of players requires to solve the game itself, we observe that for dynamic games in the robotic domain there exist exact heuristics based on the spatio-temporal occupancy of the individual players. We validate our findings in realistic autonomous driving scenarios showing that already for a 4player intersection we have a reduction of game nodes and solving time close to 99%.
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