According to foreign media reports, the University of Michigan (UM) uses the workshop communication (V2V) technology to enable autopilot vehicles to respond to other road users in a timely manner, helping them to save energy and improve safety, thus verifying the efficiency of the interconnected cruise control system.
The team demonstrated many of the benefits of Connected Cruise Control in the road test, which enabled only one self-driving car to be compared to other vehicles. Interconnected cruise control adjusts the speed of the vehicle, depending on the information acquired by its workshop communication system. The surrounding vehicles can use the wireless network to share real-time vehicle speed and positioning data.
Compared to other widely used adaptive cruise systems, the Interconnected Cruise Control System can track multiple vehicles ahead. The University of Michigan road test demonstrates how the system and the workshop communication between autonomous vehicles and traditional vehicles can help avoid "Traffic wave". In this scenario, the foremost car will trigger a "braking and re-acceleraTIng" chain reaction of other vehicles. When the self-driving vehicle with interconnected cruise control takes braking, its gravity value is reduced by 60% compared to conventional manned vehicles.
In the completion of the brake-to-acceleration operation, the energy-saving efficiency of the V2V-equipped autonomous vehicle is 19% compared to the non-interconnected and self-driving vehicles.
If the on-board sensor is the only source of information for an autonomous vehicle, when the current vehicle suddenly takes a brake operation, the response time of the vehicle is extremely short, and it is difficult to avoid a potential collision accident, which is almost the same as that of a conventional vehicle. The University of Michigan's R&D team performed a series of V2V interconnected autonomous vehicle tests that received motion information from the vehicles ahead and received information on up to six vehicles.
The research team also found that the V2V autopilot algorithm can achieve energy savings in the context of stop-and-go road conditions compared to traditional sensor-based algorithms. Due to the relatively flat speed, conventional vehicles located behind the self-driving vehicle can also achieve 7% energy savings.
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