A probabilistic approach for automated lane identification based on sensor information

ANZCC 2020, Australian and New Zealand Control Conference, GOLD COAST, AUSTRALIE, 26-/11/2020 - 27/11/2020The level lane location problem of sensor equipped vehicles circulating within arbitrary highway infrastructures is addressed. A first approach of a flexible probabilistic decision-making policy is developed utilizing sensor signals. Unmanned vehicles independently of the automation degree are related to challenging executive schemes such as adaptive cruise control systems, real time routing models involving lane changing options and speed control, platoon formation operations etc. An adaptive, closed loop methodology is presented localizing suitable detections while involving uncertainty within data, sensor vagueness and trust. The whole scheme is associated with low computational complexity where no additional investment on external devices is required. The outlined framework pronounces a significantly progressed study regarding a previously presented elementary pattern. The new model focuses in the case of invalid sensor detections due to traffic context, various environmental disturbances and failures for which no response was previously available. The effectiveness of the suggested scheme is measured when applied to detailed simulation scenarios fed by ground truth data. Different complex spatiotemporal contexts elicit varying driving profiles and pragmatic behavior-change interventions unaccessible from direct recordings provided by professional drivers. The proposed methodology is compared with a non-probabilistic model. Analysis illustrates noteworthy accuracy, precision and frequency on the resulting responses

Decision making approaches optimizing the benefits of fully autonomous and connected collective cars

HSI 2020, 13th International Conference on Human System Interaction, Tokyo, JAPON, 06-/06/2020 - 08/06/2020An Intelligent Transportation System (ITS) operating without prior reservations while offering high quality, door-to-door services at reduced fares is studied. The structure, comprised of fully autonomous cars, covers arbitrary urban areas and operates without prior reservations. Specifically developed control algorithms based on Optimization, Operations Research and Artificial Intelligence optimize the system management for any demand level and geometry. Due to V2V, V2I and V2C connectivity a fast and secure information update is achieved. Well-adapted itineraries considering customer preferences are dynamically defined. Idle vehicles are controlled and travel durations are reduced. Adequate use of the available vehicle capacity allows an important reduction of the costs for both cars and users. A comparative study with a self-service manually driven car scheme is also introduced. Qualitative and quantitative measurements appraise the system perfomance.The presented micro transit scheme in association with innovative technology (UV light sanitizing cars) could form a successful and affordable alternative for all involved entities ommuters, traffic and environment under pandemic crisis where mass public transport operators increase traveller risks

Adaptive level lane estimation policy of automotive systems moving within arbitrary road networks

VENITS 2020, 5th International Workshop on Vehicular Networking and Intelligent Transportation Systems, Honolulu, ETATS-UNIS, 06-/08/2020 - 06/08/2020The problem of a Vehicle Positioning System (VPS) is adressed. A specifically developed approach based on the cause-and-effect principle defines a closed-loop policy determining the lane level location of a sensor equipped vehicle moving within arbitrary road networks. After exploration of processed data provided by a smart camera and a laser detector appropriate information is routed to the decision making scheme. Based on identification of particular mobile objects circulating within the current and/or opposite flow the vehicle lane level location is determined when the car circulates within highway stretches or urban areas. Contrary to other schemes no additional investment on costly devices and complementary tools is required. The reduced computational complexity makes the deployment of the suggested methodology efficient for embedded technologies and can be utilized independently or as a complementary tool. Major restrictions of previous works of the same authors requiring divided freeway infrastructures are now overcome. A first appraisal of the system effectiveness is achieved though realistic simulation data fed by complex real recordings. Multiple drawbacks involved with information collected by professional driver behavior are minimized. Three case study scenarios, associated with different traffic and light intensity, resulting to a varying sensor behavioral functioning are considered. Adequate metrics appraise the efficiency of the suggested methodology

Maximum pressure controller for stabilizing queues in signalized arterial networks

ISSN:0361-1981ISSN:2169-405

Dynamic Evolution and Optimisation of an Urban Collective Taxis Systems by Discrete-Event Simulation

ITS World Congress 2016, Melbourne, Australie, 10-/10/2016 - 14/10/2016We are interested in an independent, affordable demand responsive transportation structure involving high quality of service and comfort similar to individual cars. Thereduced fares will be achieved by raising the number of car passengers while keeping vehicles as busy as possible. The high standard of service will be ensured by a smart allocation of passengers to vehicles whilst optimal car itineraries will be defined according to their present state and the current traffic conditions. Moreover, the detours which clients can tolerate will be controlled and client waiting times will be taken into consideration. The system covers an entire urban area (including the suburbs), ensuring autonomous door-to-door services and flexible operational modes while it is destined for all classes of commuters. Various strategies on the system management and dimensioning will be examined and each resulting performance will be appraised (in balance with the pending costs). Metrics on client detours, client waiting times, vehicle occupancy etc. are provided. Optimisation of all the real-time controls governing the system (e.g. client acceptance, vehicle itinerary, idle vehicle management etc.) will be achieved within a virtual but reliable environment by a made-to-measure discrete event decision tool

Data-driven feedback algorithms for automated position identification and environment reconstruction of autonomous vehicles

Independently of the automation level challenging aspects on unmanned vehicle management such as performance optimization processes, adaptive cruise control systems, steering and braking controls, are related to the assessment of the operational functioning degree of relevant responsive procedures. This paper states the problem of determining both the position and the environment of fully autonomous cars for safe and efficient car routing control. An adaptive, closed loop policy is proposed utilizing a minimum number of measurements available by principal car technology, a laser sensor and a smart camera. No additional investment is required on other devices where the costly continuous real time scans can be now reduced. The low computational complexity encourages for embedded designs providing real time responses. Missing or invalid sensor information is estimated by a first approach using neural network programming. Positions of mobile and immobile entities are determined and the related environmental context within the desired vehicle vicinity is precisely reproduced. The performance of the proposed methodology is evaluated through realistic simulation data fed by multiple ground truth recordings. Hence, multiple biases on direct information as generated by professional drivers leading to untruthful and unreliable conclusions are now minimized. A trajectory within Paris central areas and suburbs is considered involving varying traffic conditions and rich road infrastructure. Some first qualitative and quantitative results appraise the accuracy and performance of the proposed methodology while further research targets are discussed

Max-pressure controller for stabilizing the queues in signalized arterial networks

The problem of arterial signal control is considered here. Urban intersections face serious congestion problems and at the same time the installation and maintenance of centralized systems is deemed cumbersome. A decentralized approach which is relatively simple to implement is studied here. The recently proposed max-pressure controller, which provably stabilizes the queues of arterial traffic systems, is tested in simulations. Different modifications of the controller are analyzed and compared under the same demand scenarios. The mesoscopic model used for the simulation experiments is an extended version of the store-and-forward model and emulates the arterial traffic network as a queuing system. The obtained results demonstrate the efficiency of max-pressure algorithm, which, under certain conditions, can stabilize all queues of the system

Dynamic strategies optimizing benefits of fully autonomous shared vehicle fleets

21st International Conference on Intelligent Transportation Systems, ITSC 2018, Maui - Hawaï, ETATS-UNIS, 04-/11/2018 - 07/11/2018A constrained optimization framework of a flexible demand responsive transport system is considered. An intelligently administered scheme consisting of unmanned vehicles, requiring no prior seat reservation is introduced ensuring high quality door-to-door services at reduced costs. A decentralized decision making scheme comprised of various model based adaptive control patterns is developed. At any time optimized use of the available vehicle capacity is achieved while keeping cars as busy as possible. Vehicle itineraries are smartly defined according to their current state, traffic conditions and demand as well customer preferences. Tolerated passenger detours are respected while taking into consideration the related client waiting time. The asynchronous system behavior is modeled based on theory and methodology of discrete event dynamic systems (DEDS). Discrete event simulations permit evaluation of the system performance as well optimal tuning of the involved control algorithms. After identification of the desirable DEDS states the system is guided to controllable events infinitely often. As a case study, the city of Paris is considered. A comparative study is conducted appraising the suggested vehicle fleet versus a scheme consisting of selfservice autonomous vehicles (SSAV). Metrics on cars, clientsand network are presented such as trip durations, client waiting time and queue lengths at nodes, vehicle occupancy etc