Unveiling Taxi Drivers Strategies via cGAIL: Conditional Generative Adversarial Imitation Learning Smart passengerseeking strategies employed by taxi drivers contribute not only to driversu0027 incomes, but also higher quality of service passengers received. Therefore, understanding taxi driversu0027 behaviors and learning the good passengerseeking strategies are crucial to boost taxi driversu0027 wellbeing and public transportation quality of service. However, we observe that driversu0027 preferences of choosing which area to find the next passenger are diverse and dynamic across locations and drivers. It is hard to learn the locationdependent preferences given the partial data (i.e., an individual driveru0027s trajectory may not cover all locations). In this paper, we make the first attempt to develop conditional generative adversarial imitation learning (cGAIL) model, as a unifying collective inverse reinforcement learning framework that learns the driveru0027s decisionmaking preferences and policies by transferring knowledge across taxi driver agents and across locations. Our evaluation results on three months of taxi GPS trajectory data in Shenzhen, China, demonstrate that the driveru0027s preferences and policies learned from cGAIL are on average 34.7% more accurate than those learned from other stateoftheart baseline approaches.

Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.