We develop a protocol for constructing intelligent manoeuvring of driverless taxis - especially for empty taxis - with the aim of optimising the efficiency of the taxi system (e.g. minimising the average waiting time of the commuters). The manoeuvring is formulated as a stochastic policy matrix giving optimal choices on which way to go for taxis at any locations, based on the historical patterns of commuter origin and destination distribution. A recursive value (RV) model is formulated for turn-by-turn navigation. The policy matrix is solved by a combination of the RV model and reinforcement learning, together with a comprehensive agent-based microscopic simulation platform we have developed. We show that even for very large road networks, very efficient manoeuvring algorithms can be readily found with reinforcement learning, especially with our RV model as the initial state. Such algorithms are indispensable for the fleet of driverless taxis, and can also give helpful recommendations to human drivers operating taxis. The general framework of our protocol allows the construction of optimal manoeuvring algorithms for a wide range of road networks in a systematic way.


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    Title :

    Turn-by-turn Intelligent Manoeuvring of Driverless Taxis: A Recursive Value Model Enhanced by Reinforcement Learning


    Contributors:
    Yang, Bo (author) / Li, Qianxiao (author)


    Publication date :

    2018-06-01


    Size :

    1143596 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English