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Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles

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arxiv 2001.03908 v2 pith:CE4UUSWV submitted 2020-01-12 eess.SY cs.ROcs.SY

classification eess.SYcs.ROcs.SY
keywords autonomousdrivingself-drivingcontroldriverhumanlikepersonalized
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteria for the occupant's driving preference, but also provides a personalized autonomous self-driving style like a human driver instead of a Robocar. The simulation and experimental results demonstrated that the proposed system can maneuver the self-driving vehicle like a human driver by tracking the specified criterion of admissible acceleration and jerk.

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Cited by 1 Pith paper

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  1. Long-term Traffic Scene Prediction via Polynomial Representations in Autonomous Driving

    cs.AI 2026-08 conditional novelty 6.0 of 10

    Polynomial representations of trajectories and maps yield competitive prediction accuracy while substantially improving cross-dataset generalization and computational efficiency in autonomous driving.

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