Until all vehicles are equipped with autonomous driving technology, there will inevitably be mixed traffic conditions that consist of autonomous vehicles (AVs) and manual vehicles (MVs). Interactions between AVs and MVs have a negative impact on traffic flow. Cloverleaf interchanges (ICs) have a high potential to cause traffic accidents owing to merging and diverging. Analyzing the driving safety of cloverleaf ICs in mixed traffic flows is an essential element of proactive traffic management to prevent accidents. This study proposes a comprehensive simulation approach that integrates driving simulation (DS) and traffic simulation (TS) to effectively analyze vehicle interactions between AVs and MVs. The purpose of this study is to identify hazardous road spots for a freeway cloverleaf IC by integrating DS and TS in mixed traffic flow. The driving behavior data of MVs collected through a DS were used to implement vehicle maneuvering based on an intelligent driver model in the TS. The driving behavior of the AVs was implemented using the VISSIM parameters of the AVs presented in the CoEXist project. Additionally, the market penetration rate of AVs, ranging from 10% to 90% in 10% increments, was considered in the analysis. Deceleration rate to avoid crashes was adopted as the evaluation indicator, and pinpointing hazardous spot technique was used to derive hazardous road spots for the cloverleaf IC. The most hazardous road spot was identified in the deceleration lane where greater speed changes were observed. Hazardous road spots moved downstream within the deceleration lane as traffic volumes increased based on level of service. The number of AVs decelerating stably increased as traffic increased, thereby improving the safety of the deceleration lane. These results can be used to determine the critical point of warning information provision for preventing accidents when introducing AVs.

This paper describes the concept to design ship detection and identification systemwith combined use of Automatic Identification System (AIS) reports over Synthetic Aperture Radar (SAR) data. TerraSAR-X data (HH-polarization) in spotlight mode acquired on May 2, 2010 is used in this work with AIS-reports taken as ground truth. Till so far, only ship detection algorithms like CFAR (Constant False Alarm Rate), Alpha-stable distribution etc. were presented in most of the literatures for ship detection but also there are some limitation for ship detection performance like metrological conditions, image properties, speckle noise occurrence etc. Here, we present integration concept of both data by means of time matching of AIS-reports with image acquisition in order to estimate ship’s dead-reckoning (DR) location from AIS-report and are projected over an image along with the ship’s hull design for suitability and accurate reliability results. Nearest distance search method are applied to designate the SAR-derived ship targets within certain region of interest around DR. At last, DR based ship’s hull pattern is shifted over SAR-derived ship targets to conclude matched performance result in a well suitable manner.