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Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
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To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing automatic driving algorithms, without additional hardware. It has been validated through simulation experiments on the Prescan, CarSim, and Matlab platforms, demonstrating that it can increase passive recovery energy by 11.74\% and effectively track motor speed and acceleration at optimal power. To sum up, EHMPP not only aids in trajectory planning but also significantly boosts energy efficiency in autonomous EVs.
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Cited by 1 Pith paper
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"What's Happening"- A Human-centered Multimodal Interpreter Explaining the Actions of Autonomous Vehicles
A multimodal interpreter with bird's-eye view, map, text, and LLM voice explanations increased self-reported passenger trust in simulated autonomous driving, by about 8% on average and up to 30% in normal conditions.
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