Analysis of driver characteristics, self-reported psychology measures and driving performance measures associated with aggressive driving.

Whereas aggressive driving mainly causes speed-related crashes, aggressive driving may be reduced to improve road safety by identifying aggressive driving behaviour, aggressive drivers' characteristics, and their underlying motivational and psychological processes. Previous studies show that both driving performance and self-reported measures of aggressive driving are effective means to identify aggressive drivers. However, these studies assessed aggressive driving patterns across only a limited number of events, did not relate driver characteristics to aggressive driving in each event, and used chiefly vehicle kinematics variables (e.g., mean speed), but not vehicle dynamics variables (e.g., brake pedal force) which better capture driver reaction and decision-making. To address these limitations, this study assessed driver characteristics, self-reported psychological measures, and driving performance measures associated with aggressive driving among 55 drivers' behaviours in 9driving events using a driving simulator and survey responses. The results of structural equation models showed that unique aggressive driving patterns and driver characteristics related to aggressive driving vary among different driving events. As such, we recommend road safety policies to reduce aggressive driving based on the findings in this study.

Aroma of the essential oil of peppermint reduces aggressive driving behaviour in healthy adults.

Aggressive driving is of increasing concern in modern society. This study investigated the potential for the presence of an ambient aroma to reduce aggressive responses in a simulated driving situation. Previous literature has demonstrated the beneficial effect of peppermint (Mentha piperita) aroma on driver alertness and we aimed to identify any impact on aggressive driver behaviour. Fifty volunteers were randomly assigned to one of two conditions (peppermint essential oil aroma and no aroma). Aggressive driving behaviours were measured in a virtual reality driving simulator. The analysis indicated that the peppermint aroma significantly reduced aggressive driving behaviours. The presence of the aroma also produced medium sized effects on some aspects of mood from pre-test levels. These results provide support for the use of ambient aromas for the modification of driving behaviours. It is proposed that applying peppermint into daily driving may be a beneficial for reducing driver aggression.

The association between the Big Five personality traits and driving behaviors: A systematic review and meta-analysis.

Although a large number of studies have examined the relationship between the Big Five personality traits and driving behaviors, consistent evidence for their relationships is still lacking. The main purpose of this study was to systematically review the relationships between the Big Five personality traits and various driving behaviors with different intentions (including risky, aggressive, and positive driving behaviors) through a meta-analysis. A total of 34 articles met the inclusion criteria for the meta-analysis. The results showed that risky and aggressive driving behaviors were negatively associated with conscientiousness (r = -0.21; r = -0.26), agreeableness (r = -0.23; r = -0.37), and openness (r = -0.08; r = -0.07), positively associated with neuroticism (r = 0.11; r = 0.26), and nonsignificantly associated with extraversion (r = 0.06; r = -0.06). Positive driving behaviors were positively associated with conscientiousness (r = 0.30), agreeableness (r = 0.32) and openness (r = 0.20) but nonsignificantly associated with extraversion (r = 0.08) and neuroticism (r = -0.10). In addition, the association between the Big Five personality traits and driving behaviors could be moderated by age, gender and type of personality measure. In conclusion, this study contributes to the literature by quantitatively synthesizing existing findings and reconciling previous debates on the relationship between the Big Five personality traits and driving behaviors. From a practical perspective, our findings provide valuable insights into driver selection and screening, policy development, and safety intervention design.

The relationship between aggressive driving and driver performance: A systematic review with meta-analysis.

Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver's performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver's control performance among the general driver population in four-wheeled passenger vehicles and published with full text in English. These 34 selected studies involved 1731 participants in total. By examining the selected 34 studies, the measures relating to vehicle speed (e.g., mean speed, n = 22), lateral control (e.g., lane deviation, n = 17) and driving errors (e.g., violation of traffic rules, n = 12) were reported most frequently with a significant difference observed between aggressive driving and driving in the control group. The result of the meta-analysis indicates that the aggressive driving behaviour would have 1) a significantly faster speed than the behaviour in the control group with an increase of 5.32 km/h (95% confidence interval, [3.27, 7.37] km/h) based on 8 studies with 639 participants in total; 2) 2.51 times more driving errors (95% confidence interval, [1.32, 3.71] times) than the behaviour in the control group, based on 5 studies with 136 participants in total. This finding can be used to support the identification and quantification of aggressive driving behaviour, which could form the basis of an in-vehicle aggressive driving monitoring system.

Self-reported changes in aggressive driving within the past five years, and during COVID-19.

Aggressive driving is a significant road safety problem and is likely to get worse as the situations that provoke aggression become more prevalent in the road network (e.g. as traffic volumes and density increase and the grey fleet expands). In addition, driver frustration and stress, also recognised as triggers for aggression, are likely to stay high because of the COVID-19 pandemic and associated burdens, leading to increased aggression. However, although drivers report that other drivers are becoming more aggressive, self-report data suggests that the prevalence of aggression has not changed over time. This may be due to the methods used to define and measure aggression. This study sought to clarify whether self-reported aggression has increased over a five-year period and across three different types of aggression: verbal aggression, aggressive use of the vehicle and personal physical aggression. The influence of COVID-19 lockdowns on own and others' driving styles was also investigated. A total of 774 drivers (males = 66.5%, mean age = 48.7; SD = 13.9) who had been licensed for at least five years (M = 30.6, SD = 14.3), responded to an online survey and provided retrospective frequencies for their current aggression (considered pre-COVID-19 lockdowns) and five years prior. Two open ended questions were included to understand perceived changes in driving styles (own and others) during the COVID-19 pandemic. One third (33%) of drivers believed they were more aggressive now than five years ago but 61% of the sample believed other drivers were more aggressive now than five years ago. Logistic regression analyses on changes in self-reported aggression (same or decreased vs increased) showed the main factor associated with increases in aggressive driving was the perception that other drivers' aggression had increased. Further, almost half the sample (47%) reported that other drivers had become riskier and more dangerous during, and soon after, the COVID-19 lockdowns. These results show that the driving environment is seen as becoming more aggressive, both gradually and as a direct result of COVID-19 lockdowns. The data indicate that this perceived increase in aggression is likely to provoke higher levels of aggression in some drivers. Campaigns to reduce aggression on the roads need to focus on changing road culture and improving interactions, or perceived interactions, among road users.

Preliminary evidence of the efficacy of the Reducing Aggressive Driving (RAD) program.

INTRODUCTION: Aggressive driving contributes to crashes, which often result in serious or fatal injuries. Efforts to reduce road trauma need to include strategies to reduce emotional and aggressive driving. Thus far, solutions have not comprehensively addressed the reasons why drivers become aggressive. This study provides preliminary evidence of the effectiveness of the Reduce Aggressive driving (RAD) program in improving driver behavior. The RAD is based on group discussion, feedback, and goal setting to encourage more positive responses to triggers for aggressive driving. The aim of this study was to evaluate the delivery of the RAD and its impact on driver anger and aggression. METHOD: A total of 94 drivers, ranging in age from 18 to 74 years (Mean = 38; SD = 15; 56% males) attended one two-hour online RAD session during which they identified triggers for their aggression and developed individual strategies to avoid aggressive driving. Most (87%) participants agreed that the RAD helped them generate realistic strategies to avoid aggressive driving. A subset of 67 participants provided self-reported anger and aggressive driving tendencies one month, and four months after the RAD. RESULTS: When these were compared to baseline measures taken before participation in the RAD, decreases across all measures were observed. Thus, anger and aggressive driving significantly decreased one month after the RAD, and these decreases were maintained at the four month follow up; providing evidence of the effectiveness of the RAD in reducing these dangerous behaviors. Further research is needed to objectively measure changes in behavior to and support broader roll-out of the RAD program.

Modeling Motorcyclists' Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents.

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

Levels and Relationships of Impulsivity and Aggressive Driving in Shaqra, Saudi Arabia.

Background: Aggressive driving is prevalent and may be associated with impulsivity. The relationships between these variables among Saudi drivers have received scant attention. In this study, we aimed to examine the level of aggressive driving and its relationships with impulsivity among Saudi drivers in Shaqra. Methods: Overall, 504 Saudi drivers were recruited and assessed in this cross-sectional study using demographic and driving proforma, a self-reporting Barratt impulsiveness scale (BIS), and an Aggressive Driving Behavior Scale (ADBS). Results: BIS and ADBS had mean scores of 37.97 (3.24) and 21.74 (8.51), respectively. In linear regression analysis, the value of the BIS non-planning subscale negatively predicted the value of the ADBS Conflict subscale (beta = -.151, p = .002) and Speeding subscale (beta = -.103, p = .031). In contrast, the value on the score of the BIS Motor subscale statistically significantly and positively predicted the value on the score of the ADBS Speeding subscale (Beta = -.103, p = .032). Conclusion: The result shows a differential link between the component of impulsivity and aggressive driving. The lack of foresight is negatively linked with conflict behavior and high- speed driving, whereas acting without thinking is positively associated with high-speed driving.

Effects of safe driving climate among friends on prosocial and aggressive driving behaviors of young drivers: The moderating role of traffic locus of control.

INTRODUCTION: This study focused on the impact of safe driving climate among friends on prosocial and aggressive driving behaviors for young Chinese drivers, arguing for the moderating role of traffic locus of control. METHOD: Three hundred and fifty-two young Chinese drivers aged 18 to 25 years agreed to participate in this study and completed the questionnaire, which included items related to safe driving climate among friends, traffic locus of control, and prosocial and aggressive driving behaviors. RESULTS: Safe driving climate among friends and traffic locus of control had direct effects on prosocial and aggressive driving behaviors. More importantly, internal locus of control moderated the relationship between communication on prosocial driving behavior and the relationship between shared commitment to safe driving and aggressive driving behavior. External locus of control moderated the relationship between social costs and prosocial driving behavior and the relationships between shared commitment to safe driving and prosocial and aggressive driving behaviors. It can be inferred that the effects of safe driving climate on prosocial and aggressive driving behaviors varied with their levels of traffic locus of control. PRACTICAL APPLICATIONS: This study enriches current theoretical frameworks and may be applied in the development of interventions and training for young drivers from the perspective of safe driving climate among friends and traffic locus of control.

Differences in Driving Anger among Professional Drivers: A Cross-Cultural Study.

Public transport systems have a vital role in achieving sustainable mobility goals, diminishing reliance on private individual transport and improving overall public health. Despite that, transport operators are often in situations that require them to cope with complex working conditions that lead to negative emotions such as anger. The current study represents a segment of the permanent global research agenda that seeks to devise and test a psychometric scale for measuring driving anger in professional drivers. The present research is one of the first attempts to examine the factorial validity and the cross-cultural measurement equivalence of the broadly utilized Driving Anger Scale (DAS) in three culturally different countries within the Western Balkans region. The respondents (N = 1054) were taxi, bus and truck drivers between 19 and 75 years of age. The results pertaining to confirmatory factor analysis showed that there were adequate fit statistics for the specified six-dimensional measurement model of the DAS. The measurement invariance testing showed that the meaning and psychometric performance of driving anger and its facets are equivalent across countries and types of professional drivers. Furthermore, the results showed that driving anger facets had positive correlations with dysfunctional ways of expressing anger and negative correlations with the form of the prosocial anger expression. In addition, the results revealed that taxi drivers displayed considerably higher levels of anger while driving and aggressive driving than truck and bus drivers. Overall, this study replicates and extends the accumulated knowledge of previous investigations, suggesting that the original DAS remains a reliable and stable instrument for measuring driving anger in day-to-day driving conditions.

A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning.

Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F1-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance.

Aggressive driving behavior prediction considering driver's intention based on multivariate-temporal feature data.

Aggressive driving behavior is mainly motivated by the intention of the driver; therefore, the underlying intention of behavior should be considered in investigating aggressive driving behavior. However, existing aggressive driving behavior prediction methods are not advanced in a compelling of characterizing the driver's intention among a large set of attributes and describing the random process among time-varying transversion. To address this, this paper proposes a prediction method, which is structured with a Hidden Markov Model (HMM) and attention-based Long Short-Term Memory (LSTM) Network. HMM is applied to extract the driver's intention which leads to aggressive driving behavior; attention-based LSTM networks are applied in the multivariate-temporal aggressive driving behavior prediction. The method input uses panel data which contains observations about different cross-sections across time. In the case study, the model was trained based on the Shanghai Naturalistic Driving Study data. After comparing with other deep learning methods and normal LSTM, results show the proposed method provides good performance for aggressive driving behavior prediction (Mean of Accuracy = 80%), especially with the 2-sec time interval applied (Training Accuracy = 82% and Validation Accuracy = 84%). Also, the result shows that the attention mechanism can improve the result's interpretability, and using the driver's intentions as input can enhance the model accuracy. This method for predicting aggressive driving behavior that combines driver's intention, variable contribution sorting, and time-series processing. This method can be used in real-world applications for improving driving safety with the applications in the Advanced Driver Assistance Systems.

Analysis of factors influencing aggressive driver behavior and crash involvement.

OBJECTIVE: Aggressive driver behavior is one of the major contributing factors to road crashes. However, the relationship between aggressive driver behavior and crash risk is scarcely explored. The present study focused on quantifying the effect of aggressive driver behavior on crash probability. METHOD AND DATA SOURCES: A sample of 405 Indian drivers were analyzed to model the aggressive driver behavior using self-reported measures. Generalized linear models were developed to quantify the effects of independent variables such as age, gender, personality traits (e.g., driving anger, physical aggression, hostility), and individual predilections to commit violations (e.g., excessive speeding and frequent risky overtaking) on aggressive driver behavior and crash probabilities. RESULTS: K-means clustering technique was applied to the Aggressive Driving Scale (ADS) scores to cluster the drivers into three groups (aggressive, normal, and cautious). Gender was significantly correlated with aggressive driver behavior. Compared to female drivers, male drivers were 2.57 times more likely to engage in aggressive driving. Driver's age was negatively correlated with aggressive driving. With one-year increment in driver's age, the tendency of a driver to engage in aggressive driving was reduced by 26%. In addition, the likelihood of being engaged in aggressive driving was increased by 2.98 times and 2.15 times for the drivers who engage in excessive speeding and frequent risky overtaking, respectively. Driver's personality traits were significantly correlated with aggressive drivers. The crash involvement model showed that aggressive drivers were 2.79 times more likely to be involved in road crashes than cautious drivers. Further, married drivers were 2.17 times less likely to be involved in crashes, whereas for professional drivers the crash involvement probability was increased by 75%. CONCLUSIONS: The results revealed that in addition to age and gender personality traits were significant predictors of driving aggression. Further, the driver's marital status was negatively correlated with the crash involvement and professional drivers were more likely to be involved in crashes than nonprofessional drivers. The study findings can be used in identifying specific risk-prone drivers to provide safety measures via in-vehicle Advanced Driver Assistance Systems (ADAS).

Retaliatory aggressive driving: A justice perspective.

Driving anger and roadway aggression have previously been conceptualized using attributional theory, the theory of planned behavior, and the general aggression model (GAM) framework. The current study builds on these findings, testing the applicability of the attribution-of-blame model of perceptions of injustice and expanding existing models of retaliatory driving aggression to include unjust world beliefs and sensitivity to injustice. A sample of 269 participants from a large urban Canadian university viewed five animated driving scenarios (i.e., a queuing violation, a dangerous turn in front of oncoming traffic, selfish parking behavior, misuse of a high occupancy vehicle lane, and a driver failing to stop at a red light). Prior to viewing each scenario, a brief written description of the scenario was provided to each participant and read aloud by the experimenter. After viewing each scenario, participants completed a questionnaire regarding their attributions, emotions, and anticipated behavior in response to the animated scenario. After viewing all animated videos, participants completed a second questionnaire that assessed individual differences and demographic variables. Consistent with the GAM, structural equation and mediation analyses identified a significant path from individual differences (i.e., belief in an unjust world and driving injustice sensitivity), through internal states (i.e., perceptions of injustice and anger), to retaliatory aggressive driving. Results of this study found consistent paths between factors which were significant across all five scenarios and may therefore be generalizable to other driving situations. Other pathways were found to influence only a selection of the five scenarios, suggesting that they may be situation specific. Results provide support for possible intervention strategies that can be employed by driver education programs to reduce aggressive driving.

Testosterone, personality traits and aggressive driving among young male drivers.

INTRODUCTION: Psychological testing to examine potentially aggressive behaviour is a gold standard, but it is not sufficient. Testosterone might increase an aggressive behaviour. AIM: The aim of this study was to evaluate whether testosterone along with psychological assessment of fitness to drive could help to identify aggressive drivers. METHODS: Male participants (n=150) aged from 20 to 25, who possessed a driving license and drive at least 100 km per week, were evaluated in this study using an Inventory of traffic-relevant personality characteristics, the Sensation Seeking Scale and the Buss-Durkee Aggression Inventory. Saliva was collected for testosterone and cortisol measurements. The five binomial logistic models with dependent variables Caused an accident, Driving license taken away, Court trial, Intoxicated driving and Sporty self-report were tested in this study. RESULTS: The 'Intoxicated driving' model, was found to be statistically highly significant, explaining 48.8 % of the dependent variable's variance (χ2(16)=36.145, p<0.01). In this model with sensation seeking, actual testosterone and their interaction was highly significant and explained 20.4 % of intoxicated driving variability (χ2(3)=14.283, p<0.01). This was higher than sensation seeking scores only. CONCLUSION: To conclude, salivary testosterone might prove a biological marker that improves the identification of those with a high probability of aggressive driving or its subtypes (Tab. 3, Ref. 53).

Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver's driving styles.

Automated Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and acceptance of AVs' driving styles. The present study investigated the effects of the designed driving style of AV (aggressive/defensive) and driver's driving style (aggressive/defensive) on driver's trust, acceptance, and take-over behavior in a fully AV. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve driving scenarios in either an aggressive AV or a defensive AV. Results revealed that driver's trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV's driving style and driver's driving style. General estimating equations were conducted to analyze the relationships between driver's trust, acceptance, and take over frequency. The results showed that the effect of driver's trust in AVs on takeover frequency was mediated by driver's acceptance of AVs. These findings implied that driver's trust and acceptance of AVs could be enhanced when the designed AV's driving style aligned with driver's own driving style, which in turn, reduce undesired take over behavior. However, the "aggressive" AV driving style should be designed carefully considering driver safety.

Aggressive Driving Behaviours in Cannabis Users. The Influence of Consumer Characteristics.

This study analysed dangerous driving behaviours in twenty young occasional cannabis users through objective and self-reported data, studying the relationship between the two aspects. Visual function was assessed in a baseline session and after smoking cannabis, as well as speed-related behaviour in a driving simulator. The participants responded to questionnaires on sociodemographic factors, their consumption profile, and the incidence of dangerous behaviours (Dula Dangerous Driving Index; DDDI). After cannabis use, the results revealed a significant deterioration in visual function. In terms of speed management, they showed significantly greater acceleration force in the two different sections of the route, and they drove significantly faster. Our correlations indicate that males and heavier users display more risky speed management. Likewise, the heavier cannabis users admitted to increased dangerous driving behaviour, and an accident in the preceding year was associated with a trend towards aggressive driving behaviour according to the DDDI questionnaire. The findings of this study suggest that cannabis users adopt dangerous behaviours when driving, despite the effect this drug has on certain important functions, such as vision. The results suggest a need for awareness-raising and information campaigns.

Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment.

The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs.

Comparing driving behavior of humans and autonomous driving in a professional racing simulator.

Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers. This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario. A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data. In the first study, the participants' task was to achieve the fastest lap while keeping the car on the track. We grouped the resulting laps according to the performance (lap-time), defining driving behaviors at various performance levels. An extensive analysis of vehicle control features obtained from telemetry data was performed with the goal of predicting the driving performance and informing an autonomous system. In the second part of the study, a state-of-the-art reinforcement learning (RL) algorithm was trained to control the brake, throttle and steering of the simulated racing car. We investigated how the features used to predict driving performance in humans can be used in autonomous driving. Our study investigates human driving patterns with the goal of finding traces that could improve the performance of RL approaches. Conversely, they can also be applied to training (professional) drivers to improve their racing line.