Smooth longitudinal driving strategy with adjustable nonlinear reference model for autonomous vehicles

Comfort in Autonomous Vehicles (AVs) is a decisive aspect and plays an essential role in their advanced driving systems. As the comfort is directly influenced by the amount of acceleration and deceleration, a smooth longitudinal driving strategy can significantly improve the passenger’s acceptance level. Although some safe longitudinal strategies such as time-headway are introduced for AVs, the breakpoints in their speed generation models when approaching the front vehicle made discomfort behavior. In this paper, we proposed a continuous and differentiable reference speed model with a single equation to cover all possible relative distances. This model is constructed based on the well-known attributes of a hyperbolic tangent curve to smoothly change the speed of the host vehicle at the corner points. Moreover, the adjustable variables in our reference speed generator make it possible to choose between low and high-accelerate driving strategies. The experiments are performed based on several driving scenarios such as stop-and-go, hard-stop, and normal driving, and the results are compared with different reference speed models. The maximum improvement is obtained in the stop-and-go scenario, and on average, about 7.29 and 12.47% are achieved in terms of the magnitude of acceleration and jerk, respectively.