Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

By Clara Marina Martinez, Mira Heucke, Fei-yue Wang, Bo Gao, Dongpu Cao. 2018

511 citations recorded by the source corpus.

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.

Subfields: Automotive Engineering, Fluid Flow and Transfer Processes

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In Automotive Engineering

Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “algorithms for intelligent vehicles is given in [216].”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, unknown): “prediction [73], [74], planning [75], [76], [77], [78], [79], [80], E2E [81], [82], control [83], [84], [85], [86], [87], [88], [89], system [90], [91], [92], [93], hardware [94], [95], [96], soft-”
Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020, background): “has attracted extensive interest in recent years from both technology companies and research institutes [1], [2].”
Driver Fatigue Detection Systems: A Review (2019, background): “Research in the field of driver monitoring has gained momentum, specially for driver workload estimation [2], driver activity identification [3], secondary task identification [4] and driving style recognition [5].”
Internet of vehicles in big data era (2018, methodology): “For example, the GPS data which reflects the movement of vehicles can be traced to simulate the mobility model in IoV, which determines the topology of vehicular networks [96].”
Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach (2020, background): “Normal drivers, wo are positioned between the above two driving styles, are likely to make a trade-off decision by considering travel efficiency, ride comfort and drive safety [30], [31].”
Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach (2019, background): “Previous studies mainly focus on the driver attention [41] and distraction (either physical distraction or cognitive distraction) [36], driver intention [7], [9], driver styles [42], driver drowsiness and fatigue detection [10]–[12], etc.”