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

Bae, Il; Moon, Jaeyoung; Jhung, Junekyo; Suk, Hyeon‐Jeong; Kim, Taewoo; Park, Hyunbin; Cha, Jaekwang; Kim, Jin-Cheol; Kim, Dohyun; Kim, Shiho

doi:10.48550/arxiv.2001.03908

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…The simulation framework requires the setting of several parameters that are given in Table I. The boundaries for the jerk were selected according to the values proposed in [14]. The maximal jerk value is set to 2 m s −3 which models the behavior of an aggressive driver, while the comfortable jerk is set to 0.9 m s −3 which refers to a normal driving behavior.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Risk Management for Safely Automating Connected Driving Maneuvers

Grobelna

Zacchi

Schleiss

et al. 2021

2021 17th European Dependable Computing Conference (EDCC)

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Section: Resultsmentioning

confidence: 99%

Dynamic Risk Management for Safely Automating Connected Driving Maneuvers

Grobelna

Zacchi

Schleiss

et al. 2021

2021 17th European Dependable Computing Conference (EDCC)

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“…With the help of endurance tests, synthetic data are generated and compared with real traffic data in typical highway scenarios, such as cut-in maneuvers [13]. Typical indicators to describe human behavior in related works are average and maximal velocity, frequency and exceeding of speeding [14], acceleration and headway [15,16], as well as Time-to-Collision (TTC) and longitudinal distance [17]. Based on such parameters, the relative validity of the macroscopic behavior of driver models can be determined [18,19].…”

Section: Related Workmentioning

confidence: 99%

Objectively Scoring the Human-Likeness of Artificial Driver Models

Rock,

Hryhoruk,

Bleher

et al. 2023

Applied Sciences

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Several applications of artificially modeled drivers, such as autonomous vehicles (AVs) or surrounding traffic in driving simulations, aim to provide not only functional but also human-like behavior. The development of human-like AVs is expected to improve the interaction between AVs and humans in traffic, whereas, in a driving simulation, the objective is to create realistic replicas of real driving scenarios to investigate various research questions under safe and reproducible conditions. In urban traffic, driving behavior strongly depends on the situational context, which introduces new challenges not only for modeling but also for the evaluation of such models. However, current objective assessment strategies rarely consider situational context and human similarity, whereas subjective approaches are not suitable for iterative development processes. In this paper, we present a first attempt to make the plausibility and human-likeness of vehicles’ trajectories objectively measurable. A multidimensional quality function is presented that incorporates various parameters characterizing human-like driving behavior and compares each of those parameters to human driving behavior under similar conditions. Among other things, our validation results show that the presented evaluation methodology is scalable to a wide range of situations has the ability to identify model weaknesses, and is able to reflect the way people distinguish between artificial and human behavior.

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“…We begin by calculating the longitudinal acceleration based on the average initial acceleration and final acceleration of predicted trajectories. Then, by defining an admissible range for normal driving behaviors [39], the trajectories pass the test if their longitudinal acceleration falls within the admissible ranges.…”

Section: Admissibility Metricmentioning

confidence: 99%

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

Chen

Zhang

et al. 2020

Preprint

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The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the lane-exchanging scenario. The nearby vehicle trajectory needs to be predicted, from which the autonomous vehicle is controlled to prevent possible collisions. This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control. A trajectory prediction method is developed to anticipate the nearby vehicle trajectory. The Gaussian mixture model (GMM), together with the vehicle kinematic model, are synthesized to predict the nearby vehicle trajectory. A potential-field-based model predictive control (MPC) approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver. The potential field of the nearby vehicle is considered in the controller design for collision avoidance. On-road driving data verification shows the nearby vehicle trajectory can be predicted by the proposed method. CarSim simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy. The autonomous vehicle can thus safely perform the lane-exchanging maneuver and avoid the nearby vehicle.

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

Cited by 9 publications

References 10 publications

Dynamic Risk Management for Safely Automating Connected Driving Maneuvers

Dynamic Risk Management for Safely Automating Connected Driving Maneuvers

Objectively Scoring the Human-Likeness of Artificial Driver Models

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

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