Abstract
For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.
Highlights
Despite of the on-going research on self-explaining road layouts and designs [1,2], and on the computerized recognition methods of such designs and layouts, e.g., on methods that apply artificial intelligence methodology [3], setting up traffic signs (TSs) along the roads and traffic lights in road junctions and near pedestrian crossings by the transport authorities still remains a customary measure for reducing traffic safety risks in urban areas [4]
Communication facilities, e.g., to succor the TS recognition (TSR) function offered by advanced driver assistance systems (ADAS) [6] and self-driving cars [7]
Results gained via simulation implementing the inhomogeneous marked Poisson process (IMPP) model and making use of realistic data indicate that a TS-based road environment type (RET) change detection (CD) is feasible and can be used for driver assistance, though it is not suitable for initiating an immediate intervention in critical situations
Summary
Despite of the on-going research on self-explaining road layouts and designs [1,2], and on the computerized recognition methods of such designs and layouts, e.g., on methods that apply artificial intelligence methodology [3], setting up traffic signs (TSs) along the roads and traffic lights in road junctions and near pedestrian crossings by the transport authorities still remains a customary measure for reducing traffic safety risks in urban areas [4]. There are other viable alternative measures, as well as supplementary ones for the purpose These include—among many others—the installation of speed reduction markings onto the road-surface [5] and the installation of vehicle- to-infrastructure (V2I). V2I communication can be used for raising the road-awareness of car-drivers, as well as that of the intelligent and the self-driving road vehicles. It can be used for providing the human drivers and the smart vehicular systems with current traffic information with respect to the region, town, and area, on the one hand, and with some very specific dynamic information on individual vehicles in the vicinity, on the other [9]
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