Towards Green Transportation: Fast Vehicle Velocity Optimization for Fuel Efficiency

Cloud-Assisted Distributed Control System Architecture for Platooning

“…The need to access to information collected from a wide area and high computation demand for computing global optimal speed profiles suggest that Trip-Planner is implemented in CARMA Core Cloud as depicted in Figure 4. Notice that the use of cloud computing to solve global optimisation problem is also in accordance with the current literature in vehicle energy management systems [3], [18], [19], [23]. Usually, simplifying assumptions are used to solve optimisation problems, e.g., simple vehicle fuel consumption models, absence of traffic or simplified traffic models etc.…”

“…It is noted that to perform tasks within the RSM, real-time traffic data needs to be provided. Furthermore, latency for providing the speed trajectories to the connected vehicle should be small to avoid loss of performance, i.e., unexpected increase of the fuel consumption due to a delay to compute and actuate the optimal speed profiles [19]. For these reasons, the RSM is suggested to be implemented within the CARMA Edge as it can provide required communication latencies through the use of 5G communication technologies and a better description of the surrounding traffic by exploiting real-time traffic data collected from all vehicles sharing the road section.…”

Towards connected autonomous driving: review of use-cases

“…In addition, as the traffic on a road is highly dynamic and unpredictable, each vehicle needs to periodically update its optimal velocity profile based on the current traffic condition. For these reasons, the use of off-board computing for transportation systems has been proposed to assist vehicles with computation of the optimal speed profile [119,120]. When this approach for speed optimisation is adopted, each vehicle uploads its information, e.g.…”

“…By removing the human variability in following the advice speed, on average an additional 2% fuel reduction is obtained for highway driving while an improvement of 6.3% can be achieved for urban driving. The SAS proposed in [119] has been recently enhanced by IBM and Clemson University by including traffic light information [120]. Furthermore, to tackle the computational complexity, a parallel computing system was used as an off-board system to compute the global optimal speed profile.…”

“…As the solution in [119] does not consider traffic lights, vehicles can stop at intersections, thereby reducing fuel economy. Consequently, the additional idling phases increases the fuel consumption compared with the solution in [120]. When the PCC is used, the subject vehicle lacks the global information, and it can only determine its own velocity profile according to the traffic light ahead and hence cannot achieve a globally optimal solution.…”