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

Duz, Alessandra; Gimondi, Alex; Corno, Matteo; Savaresi, Sergio M.

doi:10.1109/tvt.2022.3168088

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…Although many decision-making models have incorporated prediction modules, decision-making tasks need to employ nonlinear logical reasoning while maintaining interpretability. The current status of vehicles is short-term, and considering it during modeling brings inference difficulties that can lower driving comfort [7]. In contrast, vehicle trajectory [8][9] is generated by drivers considering traffic rules, maps, and vehicle kinematics.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lane-Change Decision-Making With Long-Time Trajectory Prediction for Autonomous Vehicle

Wang,

Hu,

Wei

et al. 2023

IEEE Access

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In the process of autonomous vehicle lane changing, a reliable decision-making system is crucial for driving safety and comfort. However, traditional decision-making systems have short-term characteristics, which makes them susceptible to real-time inference from surrounding vehicles. Usually, system sacrifices driving comfort to ensure the safety of the lane change. Balancing driving safety and comfort has always been a research challenge. Long-term trajectory prediction can provide accurate future trajectories of target vehicles, providing reliable long-term information to compensate for the short-term variability of decision systems. This paper proposes a novel decision-making model with long-term trajectory prediction for lane-changing. First, we constructed a long-term trajectory prediction model to predict the trajectories of surrounding vehicles. Besides, we built a lane change decision-making model based on fuzzy inferencing, considering the predicted trajectories to infer the relative relationship between other vehicles and the self-driving car. The establishment of the fuzzy rule library considered the vehicle speed, acceleration, system delay time, driver delay time and the distance between vehicles. Finally, we created a dataset for training and testing the trajectory prediction model, and we built 4 cases simulation environments, for two or three vehicles on a straight road or curved road, respectively, to test the decisionmaking model. Experimental results show that our proposed model can ensure driving safety and improve driving comfort.

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Section: Introductionmentioning

confidence: 99%