scholarly journals Changes in Lactacidemia and Glycemia of Automobilism Race Car Drivers after Old Stock Race Category Racing

2021 ◽  
Vol 72 (5) ◽  
pp. 246-250
Author(s):  
W Martins ◽  
VAR Fernandes ◽  
M Conte
Keyword(s):  
Race Car ◽  
Race Car Drivers ◽  
Race Category

scholarly journals Assessment of Dietary Intake of Long-Distance Race Car Drivers—A Pilot Study

Sports ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 118
Author(s):  
Edem Appiah-Dwomoh ◽  
Anja Carlsohn ◽  
Frank Mayer
Keyword(s):  
Dietary Intake ◽  
Adult Population ◽  
Dietary Recommendations ◽  
Long Distance ◽  
Dietary Recall ◽  
Race Car ◽  
Race Car Drivers ◽  
The Mean ◽  
And Performance ◽  
Long Distance Race

Long-distance race car drivers are classified as athletes. The sport is physically and mentally demanding, requiring long hours of practice. Therefore, optimal dietary intake is essential for health and performance of the athlete. The aim of the study was to evaluate dietary intake and to compare the data with dietary recommendations for athletes and for the general adult population according to the German Nutrition Society (DGE). A 24-h dietary recall during a competition preparation phase was obtained from 16 male race car drivers (28.3 ± 6.1 years, body mass index (BMI) of 22.9 ± 2.3 kg/m2). The mean intake of energy, nutrients, water and alcohol was recorded. The mean energy, vitamin B2, vitamin E, folate, fiber, calcium, water and alcohol intake were 2124 ± 814 kcal/day, 1.3 ± 0.5 mg/day, 12.5 ± 9.5 mg/day, 231.0 ± 90.9 ug/day, 21.4 ± 9.4 g/day, 1104 ± 764 mg/day, 3309 ± 1522 mL/day and 0.8 ± 2.5 mL/day respectively. Our study indicated that many of the nutrients studied, including energy and carbohydrate, were below the recommended dietary intake for both athletes and the DGE.

scholarly journals Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers

2016 ◽  
Vol 55 (2) ◽  
pp. 191-207 ◽  
Author(s):  
John C. Kegelman ◽  
Lene K. Harbott ◽  
J. Christian Gerdes
Keyword(s):  
Open Data ◽  
Race Car ◽  
Vehicle Trajectories ◽  
Race Car Drivers

A Physiological Profile of High Performance Race Car Drivers

1994 ◽  
Author(s):  
James W. Lighthall ◽  
John Pierce ◽  
Stephen E. Olvey
Keyword(s):  
High Performance ◽  
Physiological Profile ◽  
Race Car ◽  
Race Car Drivers

scholarly journals Physiological Responses of Male and Female Race Car Drivers during Competition

2019 ◽  
Vol 51 (12) ◽  
pp. 2570-2577 ◽  
Author(s):  
DAVID P. FERGUSON ◽  
SAMUEL C. BARTHEL ◽  
MONTANA L. PRUETT ◽  
TODD M. BUCKINGHAM ◽  
PEYTON R. WAASO
Keyword(s):  
Physiological Responses ◽  
Male And Female ◽  
Race Car ◽  
Race Car Drivers

Nutrition recommendations for race car drivers

2018 ◽  
pp. 30-44
Author(s):  
Claire Haft ◽  
Alexandria Stanjones
Keyword(s):  
Race Car ◽  
Race Car Drivers

scholarly journals Training Champion-level Race Car Drivers Using Deep Reinforcement Learning

2021 ◽  
Author(s):  
Peter Wurman ◽  
Samuel Barrett ◽  
Kenta Kawamoto ◽  
James MacGlashan ◽  
Kaushik Subramanian ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Integrated Control ◽  
Extreme Case ◽  
Control Policy ◽  
Reward Function ◽  
Race Car ◽  
Model Free ◽  
Race Car Drivers

Abstract Many potential applications of artificial intelligence involve making real-time decisions in physical systems. Automobile racing represents an extreme case of real-time decision making in close proximity to other highly-skilled drivers while near the limits of vehicular control. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the nonlinear control challenges of real race cars while also encapsulating the complex multi-agent interactions. We attack, and solve for the first time, the simulated racing challenge using model-free deep reinforcement learning. We introduce a novel reinforcement learning algorithm and enhance the learning process with mixed scenario training to encourage the agent to incorporate racing tactics into an integrated control policy. In addition, we construct a reward function that enables the agent to adhere to the sport's under-specified racing etiquette rules. We demonstrate the capabilities of our agent, GT Sophy, by winning two of three races against four of the world's best Gran Turismo drivers and being competitive in the overall team score. By showing that these techniques can be successfully used to train championship-level race car drivers, we open up the possibility of their use in other complex dynamical systems and real-world applications.

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