Nanoscale

PloS one

Sensors (Basel, Switzerland)

[1] Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions.

[2] Dynamic visualization of the phase transformation path in LiFePO during delithiation.

[3] Towards fast-charging technologies in Li/Na storage: from the perspectives of pseudocapacitive materials and non-aqueous hybrid capacitors.

[4] Transport oil product consumption and GHG emission reduction potential in China: An electric vehicle-based scenario analysis.

[5] Portable System for Monitoring and Controlling Driver Behavior and the Use of a Mobile Phone While Driving.

[6] A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles.

[7] intelligent speed adaptation: preliminary results of on-road study in penang, malaysia

[8] Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

[9] Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers

[10] Assessing the Relation Between Emotional Intelligence and Driving Behavior: An Online Survey

[11] Aging: Older Adults' Driving Behavior Using Longitudinal and Lateral Warning Systems.

[12] A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and Distributed Genetic Algorithm

[13] A simulator study of driving behavior and mental workload in mixed-use arterial road environments.

[14] Driving scene retrieval with an integrated similarity measure using driving behavior and environment information

[15] The theory of planned behavior (TPB) and texting while driving behavior in college students.

[16] Influence of sexual appeal in roadside advertising on drivers' attention and driving behavior.

[17] the cognitive and psychological factors (personality, driving behavior, and mental illnesses) as predictors in traffic violations

[18] modelling signal controlled traffic based on driving behaviors

[19] drivingsense: dangerous driving behavior identification based on smartphone autocalibration

[20] mental health status, aggression, and poor driving distinguish traffic offenders from non-offenders but health status predicts driving behavior in both groups

[21] The impact of interlock installation on driving behavior and drinking behavior related to driving