An Efficient Eco-Planner for Autonomous Vehicles With Focus on Passengers Comfort

Speed planning is one of the tasks that a self-driving vehicle carries out. A complete planner should consider and balance passengers comfort, trip time and energy consumption. This paper proposes a computationally efficient global speed planner for autonomous vehicles that explicitly includes comfort as one of the main objectives. In particular, our approach considers the trip time as a user-specified constraint and optimizes a cost function that accounts for both energy consumption and comfort. Since passenger comfort plays a critical role for self driving vehicle, we propose a comfort model that captures different aspects: planar and vertical accelerations and the contribution of different frequency components. We test the algorithm on a realistic case study and we quantify the trade-off between energy consumption and comfort.

Automated summary

This paper proposes a computationally efficient global speed planner that includes comfort as one of the main objectives for self-driving vehicles. The planner considers energy consumption, trip time, and user-specified constraints to optimize a cost function. The algorithm is tested on a realistic case study, and the trade-off between energy consumption and comfort is quantified.