Experimental V2X Evaluation for C-V2X and ITS-G5 Technologies in a Real-Life Highway Environment

Abstract

In the coming years, connectivity between vehicles with autonomous driving features and roadside infrastructure will become more and more a reality on our roads, pursuing to improve road safety and traffic efficiency. In this regard, two main communication standards are considered as key enablers, that is ITS-G5 (based on IEEE 802.11p) and C-V2X (3GPP). To assess the real performance of these technologies, there is still need for an objective and independent one-to-one comparison of these technologies using off-the-shelf hardware under identical and real-life traffic conditions. Until today, performance evaluations are limited to simulations, emulations or individual technology assessments in real-life circumstances. In this paper, an exhaustive and fair evaluation of the technologies has been conducted in a real-life highway environment under identical conditions. Tailored evaluation tools in combination with our in-house CAMINO vehicular framework has been utilized to perform the tests and analyze the results for different wellspecified test cases. The performance evaluation shows that for the short-range technologies, C-V2X PC5 has, in general, a higher range than ITS-G5, while ITS-G5 offers lower latency than C-V2X PC5 in low-density scenarios. Long-range 4G C-V2X can be considered as an alternative for certain use cases. The outcome of this experimentation study can be used as valuable information for the further development of future (5G) connected and autonomous driving.

An Inter-operable and Multi-protocol V2X Collision Avoidance Service based on Edge Computing

“…We can appreciate how the latency fluctuates moderately between 1.5 and 4.6 ms. with a mean and standard deviation of 2.45 and 0.33, respectively. The mean value is reduced by a factor of 12 with respect to the work of [12] and lower than the experimental work performed by Maglogiannis et al [13], even they use 5G to transmit the message instead of a self-contained network in a single unit. Although, a delay increment might be generated by using the fifth generation of cellular communication, it only increases by 1 ms the delay [15].…”

“…Šandor et al [12] integrated MQTT on the proprietary software architecture AUTOSAR Adaptative arising a latency over 31 ms to transfer traffic information. In [13], an experimental evaluation for C-V2X and ITS-G5 technologies is performed. The proposal architecture includes a service layer where UDP frames from both ITS-G5 and C-V2X PC5 communication are converted into MQTT messages, with a delay of 5.5 and 37.84 ms, respectively.…”

Technology recognition and traffic characterization for wireless technologies in ITS band

“…The CAMINO framework [42] was used for the ITS-G5 and C-V2X PC5 dataset collection. CAMINO is a core The CAMINO software is implemented on the infrastructure deployed as part of the Belgian Smart Highway testbed [43]. The Smart Highway is a testbed deployed by IMEC on the E313 highway, near Antwerp, Flanders.…”

“…In our dataset collection, RSU4 is used as a transmitter, and a USRP N310 connected to RSU3 is used to capture and store the samples. Figure 6 shows the location of RSU3 and RSU4 on the Smart Highway testbed and the hardware components of each RSU [43]. To represent a wide range of traffic characteristics, different packet sizes of 300B for CA, 300B and 600B for DEN, and 600B for IVI packets were used at inter-packet intervals of 20, 50, 100, and 200 seconds.…”

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

“…The DSRC communication protocol in the United States is based on the IEEE 802.11p and the IEEE 1609 series standards, which are collectively known as wireless access in vehicular environments (WAVE) standards [ 62 ]. Similarly, the European Telecommunications Standards Institute (ETSI) has developed the ITS-G5 protocol based on the IEEE802.11p standard to support the high quality of service requirements of autonomous driving applications [ 63 ]. Wei et al [ 64 ] compared and analyzed the characteristics of wireless local area networks, WAVE, and 4G networks and proposed using a neural network model to select an appropriate communication method automatically and dynamically according to a vehicle’s density, speed, and data volume before data transmission.…”