Opportunistic sensing for road pavement monitoring

Road surface state monitoring is of main concern for road infrastructure owners. Hence dedicated measurement campaigns using laser scanning and image analysis are performed on a regular basis. Yet, this type of monitoring comes at a high labor cost and thus it is often limited in coverage and update frequency. This paper proposes opportunistic sensing as an alternative approach. Using sound and vibration sensing in cars that are on the road for other purposes and exploiting the advent of cheap communication, big data, and machine learning, timely information on road state is obtained. Results are compared to laser scanning for spatial frequencies between 0.1 and 100 cycles/m showing the applicability of the method. Results are also used for classification and labeling of road surfaces regarding their effect on rolling noise. Mapping illustrates the coverage of highways and local roads obtained in a few months with as few as seven cars

Providing over-the-horizon awareness to driver support systems

Vehicle-to-vehicle communications is a promising technique for driver support systems to increase traffic safety and efficiency. A proposed system is the Congestion Assistant [1], which aims at supporting drivers when approaching and driving in a traffic jam. Studies have shown great potential for the Congestion Assistant to reduce the impact of congestion, even at low penetration. However, these studies assumed complete and instantaneous availability of information regarding position and velocity of vehicles ahead. In this paper, we introduce a system where vehicles collaboratively build a so-called TrafficMap, providing over-the-horizon awareness. The idea is that this TrafficMap provides highly compressed information that is both essential and sufficient for the Congestion Assistant to operate. Moreover, this TrafficMap can be built in a distributed way, where only a limited subset of the vehicles have to alter it and/or forward it in the upstream direction. Initial simulation experiments show that our proposed system provides vehicles with a highly compressed view of the traffic ahead with only limited communication

A Simple and Robust Dissemination Protocol for VANETs

Several promising applications for Vehicular Ad-hoc Networks (VANETs) exist. For most of these applications, the communication among vehicles is envisioned to be based on the broadcasting of messages. This is due to the inherent highly mobile environment and importance of these messages to vehicles nearby. To deal with broadcast communication, dissemination protocols must be defined in such a way as to (i) prevent the so-called broadcast storm problem in dense networks and (ii) deal with disconnected networks in sparse topologies. In this paper, we present a Simple and Robust Dissemination (SRD) protocol that deals with these requirements in both sparse and dense networks. Its novelty lies in its simplicity and robustness. Simplicity is achieved by considering only two states (cluster tail and non- tail) for a vehicle. Robustness is achieved by assigning message delivery responsibility to multiple vehicles in sparse networks. Our simulation results show that SRD achieves high delivery ratio and low end-to-end delay under diverse traffic conditions

Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl

SAI: safety application identifier algorithm at MAC layer for vehicular safety message dissemination over LTE VANET networks

Vehicular safety applications have much significance in preventing road accidents and fatalities. Among others, cellular networks have been under investigation for the procurement of these applications subject to stringent requirements for latency, transmission parameters, and successful delivery of messages. Earlier contributions have studied utilization of Long-Term Evolution (LTE) under single cell, Friis radio, or simplified higher layer. In this paper, we study the utilization of LTE under multicell and multipath fading environment and introduce the use of adaptive awareness range. Then, we propose an algorithm that uses the concept of quality of service (QoS) class identifiers (QCIs) along with dynamic adaptive awareness range. Furthermore, we investigate the impact of background traffic on the proposed algorithm. Finally, we utilize medium access control (MAC) layer elements in order to fulfill vehicular application requirements through extensive system-level simulations. The results show that, by using an awareness range of up to 250 m, the LTE system is capable of fulfilling the safety application requirements for up to 10 beacons/s with 150 vehicles in an area of 2 × 2 km2. The urban vehicular radio environment has a significant impact and decreases the probability for end-to-end delay to be ≤100 ms from 93%–97% to 76%–78% compared to the Friis radio environment. The proposed algorithm reduces the amount of vehicular application traffic from 21 Mbps to 13 Mbps, while improving the probability of end-to-end delay being ≤100 ms by 20%. Lastly, use of MAC layer control elements brings the processing of messages towards the edge of network increasing capacity of the system by about 50%