Roadside Infrastructure Units Research Articles

Intelligent algorithms for incident detection and management in smart transportation systems

Prior research on traffic event detection has encountered two problems: limited sample numbers and unbalanced datasets. Moreover, the real-time properties of event detection models must be enhanced to meet traffic management demands. To solve these issues, suitable measures must be implemented, like developing an intelligent algorithm for incident detection employing Artificial Intelligence (AI) and Machine Learning (ML). Automated Incident Detection (AID) methods are the focus of current Intelligent Transportation System (ITS) technology. Modern vehicles can connect with one other and with Roadside Infrastructure Units (RSUs) to improve road safety thanks to advancements in wireless connectivity and sensor technology. Deep Learning (DL)-based methods have recently demonstrated strong performance in computer vision issues involving complicated feature associations. This work proposes a Hybrid Deep Learning-based Automated Incident Detection and Management (HDL-AIDM) system to identify traffic incidents and improve traffic management. In the suggested model, a Temporal and Spatial Stacked Autoencoder (TSSAE) is used to collect temporal and spatial associations of traffic conditions and identify events. At the same time, a generative adversarial network (GAN) is employed to improve the number of samples and equalize datasets. The proposed model is assessed from many perspectives using the dataset from real-world situations. Based on the HDL output for AID, an efficient and intelligent traffic management algorithm has been accomplished using Road Side Units (RSUs) to gather traffic information. The suggested incident management algorithm considers lane shifts and the fluctuation in vehicle speed over time, which are heavily influenced by traffic incidents. These developments in ITS enable traffic management systems to use information gathered from HDL-based AID methods using TSSAE and GAN. The proposed strategies have been devised to notify drivers about traffic issues and help them avoid congestion. The suggested method provides higher incident detection rates with an accuracy of 94.1%, a 3.9% false alarm rate, and an incident classification rate of 93.3%.

Randomized Link Repair Reactive Routing Protocol for Vehicular Ad Hoc Network

Background and Objective: Vehicular Ad hoc Network (VANET) now-a-days is popular research area due to numerous challenges such as congestion control, network management, and security issues. Methods: VANET is a subtype of Mobile Ad hoc Network (MANET), therefore, allows a vehicle to exchange information with neighboring vehicles and with roadside infrastructure units. High mobility of vehicles results to frequent link breakages. The existing VANET routing protocols follow a trivial link repair mechanism. Therefore, there is a need to compute stable and reliable routes for vehicular communication as well as frequently repair the broken links. Ad hoc On-demand Distance Vector (AODV) routing protocol is a reactive routing protocol which is suitable for very dynamic environment such as VANET. In this paper, an improved Randomized Link Repair based AODV (RLRAODV) routing protocol is proposed which uses the multiple route reply packets for data transmission through alternate paths during link failure. The proposed methodology also reduces the network congestion overhead generated due to broadcasting of route request packets. Results and Conclusion: The simulation analysis shows that RLR-AODV gives better performance with respect to performance parameters such as average end-to-end delay, average packet delivery ratio, and average packet loss ratio as compared to AODV and AOMDV.

Diffie–Hellman process and its use in secure and authenticated VC networks

The automotive companies nowadays are working on the development and deployment of the future intelligent transportation systems and connected-autonomous driving addressing any security and privacy concerns of users. In the field of connectivity, vehicular networks are constructed to manage communications among nearby vehicles and between vehicles and road-side infrastructure units and trusted agencies or certificate authorities. The above cooperative systems applications bring the promise of improved road safety and optimised road traffic. For the successful deployment of vehicular communications (VCs), it is essential to make sure that ‘life-critical safety’ information cannot be modified by external or internal within the network attackers. On this basis, lack of such security and privacy in vehicular networks is one of the key hindrances to the widespread implementations of connected-autonomous-driving applications. Moreover, specific operational parameters (highly moving vehicles, frequently and fast changed connectivity) make the problem very novel and challenging. For the present study, a protocol for data privacy and authenticated VC interactions, based on the Diffie–Hellman algorithm, is applied. The above analysis aims to improve the security and enhance the efficiency of VCs toward the deployment of Internet of vehicles technology in the transport area.

Automatic Incident Detection in Intelligent Transportation Systems Using Aggregation of Traffic Parameters Collected Through V2I Communications

Recent research in Intelligent Transportation System (ITS) focuses on Automatic Incident Detection (AID) techniques. Using advances in wireless networking and sensor technologies, modern vehicles have the ability to communicate with each other as well as with roadside infrastructure units (RSUs) in order to increase road safety. These new innovations in transportation technology provide traffic management system the ability to use data collected from the vehicles on the road to detect congestion and traffic incidents. Lately, many techniques were developed to alert drivers in advance about traffic incidents and to enable them to avoid congestion. In this paper, we discuss probabilistic collection of traffic data through vehicle-to-infrastructure (V2I) communications and present two novel techniques for automatic detection of traffic incidents in a highway scenario that are based on the use of distance and time for changing lanes, respectively vehicle speed changes over time. The proposed methods, which are illustrated with numerical results obtained from simulations, outperform alternative AID techniques through higher incident detection rates, about 25% shorter peak queue values and 20% faster dissipation of roadway congestion.

Improving Relay Selection Scheme for Connecting VANET to Internet over IEEE 802.11p

Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other (V2V) as well as with roadside infrastructure units (V2I). These units provide different services such as driver information systems and Internet access. When one node will connect to the Internet, first it must discovery the route to these units. Based only on mobility parameters, we can select longest life time routes to routing packet, but these routes may be with low quality of signal. So, a dropping packet and enforcing retransmission will be certainly happened. For that reason, we propose, in this paper, an efficient routing protocol that improve relay selection scheme by considering link life time and quality of signal as parameters of relay selection, in order to make vehicles able to select longest life time and best quality of signal route to the roadside units. Our relay selection scheme consists to select next forwarding node based on predicted life time of links, received signal strength indicator (RSSI) and signal to interference-noise rate (SINR). The objective is to be sure that data packets will be routed on longest life time, highest received signal strength and strongest routes. The simulation results show that the proposed protocol enhances throughput and decreases the packet loss and overhead with comparable end to end delay.

RSU-Based Distributed Key Management (RDKM) For Secure Vehicular Multicast Communications

Although lots of research efforts have focused on group key management (GKM) for secure multicast, existing GKM schemes are inadequate for vehicle communication (VC) systems since they incur unnecessary rekeying overhead without considering the characteristics of VC systems such as Vehicle-to-Infrastructure communications and a great number of high mobility vehicles. Therefore, we propose a GKM scheme, called RSU-based decentralized key management (RDKM), dedicated for the multicast services in the VC systems. The RDKM scheme significantly reduces the rekeying overhead through delegating a part of the key management functions to the road-side infrastructure units (RSUs) and through updating the key encryption keys (KEKs) within a RSU. The performance of the RDKM scheme is analyzed in terms of communication overhead and storage overhead each of which has a strong impact on the performance of GKM. Furthermore, we propose an optimization algorithm that minimizes the weighted sum of the communication and the storage overhead, called the GKM overhead (GKMO), by appropriately determining the design parameters. The numerical results from the extensive analysis demonstrate that the RDKM scheme outperforms the existing GKM schemes in terms of the GKMO.

An efficient routing protocol for connecting vehicular networks to the Internet

Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other (V2V) as well as with roadside infrastructure units (V2I). These units provide different services such as driver information systems and Internet access. The high speed and high mobility of vehicles make it very challenging to establish and maintain a connection to these units. We introduce a new protocol which uses the characteristics of vehicle movements to predict the future behavior of vehicles, and to select a route with the longest lifetime to connect to the wired network. The proposed protocol aims at spreading the advertisement messages through multi-hops without flooding the network and performing seamless handovers. The proposed protocol is simulated by considering mobility scenarios, generated by the MOVE tool, in the ns-2 simulator tool. The simulation results show that the proposed scheme increases the packet delivery ratio and decreases the packet delay and overhead.