Lane Change Driver Assistance System for Online Operation Optimization of Connected Vehicles

During recent years, all commercial vehicle manufacturers have introduced legally required and advanced predictive functionalities for their on-highway fleets. Telemetry, navigation and electronic horizon systems are the key elements for increasing safety, enabling congestion-free highways, reducing fuel consumption and pollutant emissions under real driving conditions. In this work a novel approach for lane change driver assistance is presented to advise the driver economically to change or keep in lane by using model predictive Adaptive Cruise Control (ACC). The system calculates lane change costs considering detected and connected traffic participants for an 8 km prediction horizon. The novel approach applies velocity loss minimization (VLM) and advanced nonlinear online energy optimization methods. This modular approach can interact with existing conventional and hybrid energy management systems. A final lane change judgment is based on the cost optimization of speed loss, gear selection and fuel consumption trajectories. This approach converges to global optimum fuel consumption under certain traffic flows. Finally, the system is applied to a 40-ton truck in a German macroscopic highway traffic simulation. In a typical highway traffic flow range between 75,000 and 95,000 vehicles per 24 hours on a three-lane highway, an average fuel consumption saving of 6% can be achieved, while the travel time criteria remains below 1%.