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Drivers and Barriers to Implementation of Connected Vehicles

By: Sam Mahdavian & Alireza Shojaei

Summarized Description:

For the gains brought by CV technology to come to fruition, approximately 80% of vehicles should support signal phase and time (SPAT) information. Certain factors influence the efficacy of connected vehicle systems, including cellular coverage, the number of vehicles equipped with DSRC, and the presence of additional ITS facilities. Most importantly, trust must be built between data owners and vehicle users in order to persuade additional customers to adopt this technology. Concerns about connectivity, privacy, and data security remain a significant implementation challenge. Consumers are divided on whether the benefits of increased connectivity in their vehicles are worth the security issues. They are cautious about whom they can entrust with the data collected and shared by vehicles, and they do not necessarily view the original equipment manufacturers (OEMs) as the most rational choice. Policies that could increase trust among users should first be identified. Openness and accuracy are essential to earning the trust of both the public and regulators. Employing developing technologies such as blockchain could play a vital role in satisfying the demand for transparency and drive the change towards a fully connected infrastructure. To increase the number of DSRC-equipped vehicles, authorities could use incentives to equip those commercial and government vehicles that have a significant presence on city streets – comprising more than 25% of the traffic – with emerging technologies. Taxis, commercial, and government vehicles are therefore an appropriate starting point for increasing the market penetration and familiarizing people with CASE vehicle technologies. They can help to build trust and, more importantly, encourage a culture of shared-mobility transport. Ultimately, the 5G technology for vehicle-to-infrastructure (V2I) systems must be tested and compared with DSRC regarding reciprocal safety messages. There have been many signs of progress in vehicle communications and AI development in the field of CVs. Several studies have been conducted on issues such as enhancing the CACC’s communication vulnerabilities and impairments such as packet loss and latency. Ploeg et al., 2015 , introduce a control strategy for graceful degradation of one-vehicle look-ahead CACC, based on determining the preceding vehicle’s acceleration utilizing onboard sensors. To handle the inevitable communication losses, Harfouch et al., 2018, have formulated a comprehensive average dwell-time framework and designed an adaptive switched control strategy. Ultimately, Xing et al., 2019, have also applied the Smith predictor and proposed a control scheme to compensate for the communication delays in a CACC Systems. There is a need to move from reactive to preventative safety. ADAS systems currently react to road, weather or traffic conditions, and all AVs’ sensors capture movement, changes, intersecting threats and react to avoid them. Various stakeholders must collaborate and invest in proactive machine intelligence technologies to process the information so that the vehicle can know in advance of a potential problem and threat and can preventatively adjust speed, course, and trajectory well in advance of the specific problem location.


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