A Sensor Fused Rear Cross Traffic Detection System Using Transfer Learning

Recent emerging automotive sensors and innovative technologies in Advanced Driver Assistance Systems (ADAS) increase the safety of driving a vehicle on the road. ADAS enhance road safety by providing early warning signals for drivers and controlling a vehicle accordingly to mitigate a collision. A Rear Cross Traffic (RCT) detection system is an important application of ADAS. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions. The RCT detection system detects obstacles at the rear while the car is backing up. In this paper, a robust sensor fused RCT detection system is proposed. By combining the information from two radars and a wide-angle camera, the locations of the target objects are identified using the proposed sensor fused algorithm. Then, the transferred Convolution Neural Network (CNN) model is used to classify the object type. The experiments show that the proposed sensor fused RCT detection system reduced the processing time 15.34 times faster than the camera-only system. The proposed system has achieved 96.42% accuracy. The experimental results demonstrate that the proposed sensor fused system has robust object detection accuracy and fast processing time, which is vital for deploying the ADAS system.

Capacity Gains of Splitting Cross-Traffic into Multiple Sub-Streams

Traffic flow might be limited by cross-traffic which has priority. A typical example of such a situation is a location where cyclists or pedestrians cross a stream of car traffic. Splitting the cross-traffic into two separate sub-streams (for instance left–right and right–left) can increase the capacity of the main stream. This is because it is no longer necessary to have a sufficiently large gap in both sub-streams simultaneously. This paper introduces a method to compute the resulting capacity of roads with cross-traffic. Without loss of generality, we introduce three transformations to simplify computations. These transformations are an important contribution of the paper, allowing us to create scalable graphs for capacity. Overall, the research shows that splitting a crossing stream into two equally large sub-streams increases the capacity of the main stream. If there is place for one vehicle in between two sub-streams, the capacity can increase up to threefold. Even larger gains are possible with more vehicles in between. This paper presents graphs which can be used to find the capacity for generic situations, and can be used for developing guidelines on intersection design.

THE PRESENCE OF THE MODERN INFRASTRUCTURE IN THE MIDDLE OF THE TRADITIONAL COMMUNITIES IN EAST TIMOR

Objective: The aim of this study is to find negative impacts of the construction of the modern infrastructure of the toll road and find out a good solution in East Timor. Methods: The research methodology used is descriptive qualitative research methodology, namely, collecting data, interviewing key persons, making data deduction, analyzing data, drawing conclusions, and expressing recommendations, especially for the contractors and for the government. Results: The construction of the toll road infrastructure megaprojects in the new country of Timor-Leste has both positive and negative impacts. The negative impact is huge and this is not taken into account by the contractors and the government in the construction of the Suai-Beasu toll road that connects the city of Suai on the South coast and the town of Dili on the North coast of the Democratic Republic of Timor-Leste. Hence, the recommendations suggested for the contractors and the government are to implement the aspirations of the communities, especially in the fair and just payment for the area used for the projects and building new cross-traffic for the residents alongside the toll road. Conclusion: The presence of the toll road had positive impacts but there are several negative impacts on the lives of the Suai people, therefore, it is better that if the government and the contractors pay serious attention to overcome the negative impacts.

The Road Not Taken: Re-thinking the Feasibility of Voice Calling Over Tor

Anonymous VoIP calls over the Internet holds great significance for privacy-conscious users, whistle-blowers and political activists alike. Prior research deems popular anonymization systems like Tor unsuitable for providing the requisite performance guarantees that real-time applications like VoIP need. Their claims are backed by studies that may no longer be valid due to constant advancements in Tor. Moreover, we believe that these studies lacked the requisite diversity and comprehensiveness. Thus, conclusions from these studies, led them to propose novel and tailored solutions. However, no such system is available for immediate use. Additionally, operating such new systems would incur significant costs for recruiting users and volunteered relays, to provide the necessary anonymity guarantees.It thus becomes an imperative that the exact performance of VoIP over Tor be quantified and analyzed, so that the potential performance bottlenecks can be amended. We thus conducted an extensive empirical study across various in-lab and real world scenarios to shed light on VoIP performance over Tor. In over half a million calls spanning 12 months, across seven countries and covering about 6650 Tor relays, we observed that Tor supports good voice quality (Perceptual Evaluation of Speech Quality (PESQ) >3 and one-way delay <400 ms) in more than 85% of cases. Further analysis indicates that in general for most Tor relays, the contentions due to cross-traffic were low enough to support VoIP calls, that are anyways transmitted at low rates (<120 Kbps). Our findings are supported by concordant measurements using iperf that show more than the adequate available bandwidth for most cases. Hence, unlike prior efforts, our research reveals that Tor is suitable for supporting anonymous VoIP calls.

General Identifiability Condition for Network Topology Monitoring with Network Tomography

Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be “zoomed in or out” by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately.