Typical-driving-style-oriented Personalized Adaptive Cruise Control design based on human driving data

Zhu, Bing; Jiang, Yuanfei; Zhao, Jian; He, Rui; Bian, Ning; Deng, Weiwen

doi:10.1016/j.trc.2019.01.025

Cited by 83 publications

(29 citation statements)

References 24 publications

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“…Wang et al [29] indicate that THW is a typical variable that describes the characteristics of a driver. Furthermore, Bing Zhu et al [30] also analyze the differences of THW among different drivers, and the THW of aggressive and cautious drivers are 1.32 seconds and 2.14 seconds, respectively.…”

Section: A Car-following Behavior Analysismentioning

confidence: 99%

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Chu

et al. 2021

IEEE Access

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Autonomous driving is one of the promising technologies to tackle traffic accident and congestion problems nowadays. Even though an autonomous vehicle is operated without humans, it is necessary to reflect the driving characteristics of a human driver. This can increase user acceptance to autonomous driving system, which in turn will improve driving safety because of human occupants' trust in it. In this paper, a combined trajectory planning and tracking algorithm is proposed for the vehicle control. Firstly, traffic environments and driving styles are modeled with the Artificial Potential Field (APF) approach. Secondly, those APF values are integrated into the Model Predictive Control (MPC) design process, which can optimize the trajectories and control outputs. In this way, we add people's driving habits and styles into the controller, so that the controlled vehicle can move under the effects of the traffic environments and human's driving styles. At last, autonomous driving, which reflects two types of human drivers' driving styles (a cautious driving style and an aggressive one), is tested by the simulation experiments in two scenarios (carfollowing and lane-changing). Furthermore, the result demonstrates that the proposed algorithm can reflect driving styles. Accordingly, this novel controller can be utilized in the autonomous vehicle control field.

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Section: A Car-following Behavior Analysismentioning

confidence: 99%

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Chu

et al. 2021

IEEE Access

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“…Most previous studies pertaining to practical applications of personalization have proposed a set of predefined values by categorizing drivers into several groups [35][36][37][38]. For example, three predefined clusters for acceleration profile parameters were proposed.…”

Section: Problem Statements and Related Work 121 Personalization In The Automotive Fieldmentioning

confidence: 99%

Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction

Kim¹

2021

Sensors

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As worldwide vehicle CO2 emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.

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“…As for the predetermined bus route, [27,[92][93][94] proposed a length ratio-based neural network energy management strategy for online control of PHEB to reduce the computational time and storage capacity of the micro-controller and to achieve approximate optimal control performance. The length ratio representing the space domain was chosen as the input variable of the neural network module to represent trip information, which consists of four parameters: trip length, trip duration, current driving length and current driving time.…”

Section: Neural Network-dynamic Programming (Nn-dp)mentioning

confidence: 99%

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

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Typical-driving-style-oriented Personalized Adaptive Cruise Control design based on human driving data

Cited by 83 publications

References 24 publications

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Combined Trajectory Planning and Tracking for Autonomous Vehicle Considering Driving Styles

Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

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