Aggressive Drivers Research Articles

Don’t sweat the small stuff; anger rumination and lack of forgiveness are related to aggressive driving behaviours

Aggressive drivers pose a significant road safety threat to themselves and other road users. Therefore, understanding the relationships between certain cognitive processes and increased frequency of aggression has the potential to reduce road trauma through intervention. This study examined the relationships between anger rumination and forgiveness with trait driving anger and aggressive driving. These factors have previously been individually identified as predictors of aggression; but have yet to be considered simultaneously, despite recognised association between forgiveness and rumination tendencies outside of road safety research. Aggressive driving was measured across three facets of behaviour: verbal aggression, physical aggression, and use of the vehicle to display anger. Adaptive constructive responses to driving anger were also considered. Five hundred and one drivers (mean age = 43.7; SD = 17.7; men = 53.8%) completed an online questionnaire seeking information on these factors. Structural equation modelling showed that, after controlling for gender, 62% of the variance in aggressive driving was explained by a combination of trait driving anger, more frequent rumination and lower levels of forgiveness. Specifically, lower levels of forgiveness predicted more anger rumination (accounting for 49% of the variance), and the relationship between anger rumination and aggression was fully mediated by trait driving anger. A second model demonstrated that 17% of the variance in adaptive constructive responses to anger could be explained by higher levels of forgiveness, lower rumination and driving anger. Efforts to improve driver behaviour need to focus primarily on the reduction of driving anger. This could be achieved by reducing rumination through the promotion of forgiveness for the behaviour of other drivers.

Traffic Order Analysis of Intersection Entrance Based on Aggressive Driving Behavior Data Using CatBoost and SHAP

Analyzing road risks and developing targeted countermeasures are essential for a safe and orderly traffic flow. However, previous intersection safety analyses were conducted based on crash data. Little research has been conducted on surrogate safety measures based on risky driving behavior. In this study, categorical boosting (CatBoost) and Shapley additive explanation (SHAP) were used to analyze the impact of features on traffic order using a set of multisource data that include roadway geometry, signal control, and land use. The traffic data for intersection entrances in Beijing were collected from navigation systems, field investigations, and application programming interfaces. The model results showed that CatBoost exhibits a prediction accuracy of 83.5%, a recall of 83.5%, and an F1 score of 81.1%. Moreover, the importance, total effects, main effects, and interaction effects of influence factors were analyzed by using SHAP. It was found that the congestion index (CI) has significant negative effects on traffic order. A larger number of lanes and more electronic traffic control were found to have a positive effect on traffic order. Intersection entrances with three-phase signals or off-peak intersection entrances helped increase traffic order. Moreover, a high green ratio for through vehicles can reduce the positive impact of CI on traffic order when the value of CI is 1.1–1.4, and the signal control scheme with a high left-turn green ratio would result in a safe and orderly traffic flow. The results from this study can be used for further studies on improving traffic safety at signalized intersections.

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.

Molecular Profiles of Noninvasive, Minimally Invasive, and Invasive Follicular Patterned Thyroid Neoplasms with Papillary Nuclear Features.

Background: An increasing amount of data is being published, which risk-stratify thyroid tumors according to genetic signatures and histological morphology. Typically, follicular patterned lesions have been shown to harbor RAS-like mutations with more indolent behaviors. Our study aims to examine the extent of similarity among three groups of follicular patterned lesions with papillary nuclear features-noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) with capsular invasion and/or angioinvasion, and infiltrative follicular variant of papillary thyroid carcinoma (iFVPTC)-to help clarify whether NIFTP and EFVPTC represent a histological continuum and the degree to which the genomic landscape further separates higher risk follicular patterned tumors such as iFVPTC from more indolent ones (EFVPTC and NIFTP). Methods: ThyroSeq test results were compared for cases with histological NIFTP, EFVPTC, and iFVPTC in this retrospective study. Genetic drivers were subcategorized by level of aggressiveness. Gene expression alterations (GEAs) and copy number alterations (CNAs) were compared among the three histological groups. Results: NIFTP and EFVPTC cases displayed predominantly RAS-like alterations (100% and 75%, respectively) and RAS-like GEAs (55.2% and 47.2%, respectively), and many showed CNAs with 22q-loss. Despite a predominance of RAS-like alterations, EFVPTC cases showed molecular heterogeneity with significantly more intermediate and aggressive drivers (22.3% of cases) than NIFTP (0%) (p = 0.0068). iFVPTC cases displayed molecular profiles in between that of traditional follicular patterned lesions and classical papillary thyroid carcinoma, predominantly displaying intermediate and aggressive drivers (61.6%), which was significantly higher than that of EFVPTC (22.3%, p = 0.0158) and NIFTP (0%, p < 0.0001), illustrating the higher MAP kinase activity of iFVPTC. There was no significant difference, however, in comparing GEAs among the three histological groups. Conclusions: While follicular patterned lesions with papillary nuclear features overall tend to display RAS-like alterations, EFVPTC cases, followed by iFVPTC in this series, showed increasing proportions of more aggressive drivers. EFVPTC and NIFTP show much molecular overlap, with predominance of RAS-like alterations, suggesting that these tumors are part of a genetic continuum, while still ranked differentially. Preoperative molecular testing can potentially distinguish EFVPTC and iFVTPC from NIFTP based on a particular molecular signature, optimizing patient management.

Do you really want to hurt me?: Exploring the role of Narcissism, driver comparison and ego threat in driver aggression using a conceptual definition of aggression

The present study examined the extent to which narcissism and driver comparisons contribute to driver aggression and aimed to determine whether these relationships were moderated by threats to one’s driving ego. In doing so, the research sought to define and operationalise driver aggression by replicating how it is defined in the general aggression literature - as behaviour intended to harm. A total of 286 participants (188 women) were recruited from a community sample of Australian drivers who completed an online questionnaire and watched a video vignette showing a provocative driving event. As expected, narcissism and driver comparison predicted higher levels of driver aggression, and aggression was higher in conditions where a driver’s ego was threatened. However, there was no interactive effect of ego threat and narcissism or driver comparison on driver aggression. Our findings have implications for the development of a consistent, theoretically grounded definition of driver aggression that focuses on intention to harm, and for the development of measures that operationalise the construct in this manner.

A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles

Driving style may have an important effect on traffic safety. Proactive crash risk prediction for lane-changing behaviors incorporating individual driving styles can help drivers make safe lane-changing decisions. However, the interaction between driving styles and lane-changing risk is still not fully understood, making it difficult for advanced driver-assistance systems (ADASs) to provide personalized lane-changing risk information services. This paper proposes a personalized risk lane-changing prediction framework that considers driving style. Several driving volatility indices based on vehicle interactive features have been proposed, and a dynamic clustering method is developed to determine the best identification time window and methods of driving style. The Light Gradient Boosting Machine (LightGBM) based on Shapley additive explanation is used to predict lane-changing risk for cautious, normal, and aggressive drivers and to analyze their risk factors. The highD trajectory dataset is used to evaluate the proposed framework. The obtained results show that i) spectral clustering and a time window of 3 s can accurately identify driving styles during the lane-changing intention process; ii) the LightGBM algorithm outperforms other machine learning methods in personalized lane-changing risk prediction; iii) aggressive drivers seek more individual driving freedom than cautious and normal drivers and tend to ignore the state of the car behind them in the target lane, with a greater lane-changing risk. The research conclusion can provide basic support for the development and application of personalized lane-changing warning systems in ADASs.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers’ collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers’ collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers’ TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Abstract 6202: 6-Phosphofructo-2-kinase in regulating DNA damage and repair in EGFR-driven lung cancer

Abstract Activating mutations of epidermal growth factor receptor (mutEGFR) are established drivers of lung tumor development, aggressiveness, and resistance to targeted therapies. While targeting mutEGFR with small-molecule tyrosine kinase inhibitors (EGFR-TKIs) dramatically improved progression-free survival in patients with non-small cell lung cancers (NSCLCs), intrinsic or acquired resistance to TKI therapy limits the duration of an effective response to TKIs. Alteration in DNA damage response (DDR) plays a vital role in genomic stability and cancer progression and helps cells to escape apoptosis, limiting the efficacy of targeted therapies. Interestingly, lung cancer cells exposed to EGFR-TKIs demonstrate attenuated glycolytic flux - one of the major providers of precursors for de novo nucleotide synthesis. A key regulator of glycolysis is the enzyme 6-phosphofructo-2-kinase (PFKFB3). PFKFB3 synthesizes fructose 2,6-bisphosphate (F2,6BP), which is a potent allosteric activator of the glycolytic rate-limiting enzyme, 6-phosphofructo-1-kinase (PFK1). Accordingly, F2,6BP controls flux throughout the entire glycolytic pathway. In preliminary studies, we provide evidence that PFKFB3 plays multiple roles in regulating the efficacy of DDR in lung cancer cells exposed to EGFR-TKIs. We show that PFKFB3 is required for EGFR-driven glucose metabolism, which provides a list of intermediates utilized in nucleotide synthesis. PFKFB3 regulates the expression of ribonucleotide reductase small subunit M2 (RRM2) of the ribonucleotide reductase (RNR), which is important for de novo onsite synthesis of deoxynucleotide triphosphate (dNTP) during DNA replication and repair. Moreover, a PFKFB3 inhibitor, PFK-158, increases oxidative stress in mutEGFR lung cancer cell lines and, as a result, promotes DNA damage. Importantly, we found that PFKFB3 plays an important role in the chromatin binding of scaffold proteins regulating the assembly of DNA repair complex. Our studies suggest that PFKFB3 plays an important role in DDR and provide a clear rationale to propose the use of PFKFB3 inhibitors in combination with EGFR inhibitors to increase the efficiency and/or overcome resistance to EGFR-TKIs. Citation Format: Nadiia Lypova, Lilibeth Lanceta, Susan Dougherty, Jason Chesney, Yoannis Imbert-Fernandez. 6-Phosphofructo-2-kinase in regulating DNA damage and repair in EGFR-driven lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6202.

Abstract 3933: RRM2 inhibition by COH29 is a potential therapeutic strategy for atypical teratoid rhabdoid tumors

Abstract Introduction: Atypical teratoid rhabdoid tumors (ATRT) are a rare but aggressive malignancy in central nervous system, commonly occurring in early childhood. ATRT patients face a dismal prognosis despite aggressive therapy. Development of both effective and protective targeted therapy is an emerging necessity. It has been reported that RRM2 is a master driver of aggressiveness and a poor prognosis biomarker in several cancers. However, little is known about RRM2 function in ATRT. The aim of this study is to assess the roles of RRM2 in ATRT and elucidate the therapeutic potential of RRM2 to cure the ATRT. Methods: Expression of RRM2 was evaluated by molecular profiling analysis and was confirmed by IHC in both ATRT patients and PDX tissues. Follow-up in vitro studies used 3 different cell lines to elucidate the oncogenic role of RRM2 in ATRT cells. The efficacy of COH29, an RRM2 inhibitor, was assessed in vitro. ATRT model of SCID mice orthotopically injected with ATRT cells were used to test the efficiency of COH29 for in vivo. Western blot and RNA-sequencing were used to determine the mechanisms of RRM2 transcriptional activation in ATRT. Results: Molecular profiling results revealed that RRM2 was relatively highly expressed in ATRT tissues and cell lines. Clinical relevance analysis results showed that the RRM2 expression level was associated with poor prognosis among ATRT patients. Knockdown of RRM2 by shRNAs significantly reduced ATRT cells colonies formation, proliferation and migration. A similar but more pronounced inhibitory effect was observed when COH29 was used to target RMM2. Inhibition of RRM2 by shRNAs or COH29 significantly induced cell death via DNA damage and/or apoptosis pathways. In vivo results demonstrated that COH29 could suppressed ATRT tumor growth and extend mice overall survival. Conclusion: Our study identified RRM2 as a potential mediator of oncogenic cellular functions in ATRT cell lines. We propose that highly expressed RRM2 is associated with poor patient outcomes, and inhibition of RRM2 by COH29 may present a significant therapeutic value in ATRT. Citation Format: Le Hien Giang, Tai-Tong Wong, Che- Chang. RRM2 inhibition by COH29 is a potential therapeutic strategy for atypical teratoid rhabdoid tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3933.

Antecedents of tax aggressiveness of listed non-financial firms: Evidence from an emerging economy

The study examined the antecedents of aggressive tax behaviour among listed non-financial firms in Ghana. With firm-level data for 19 non-financial firms covering the period 2010–2019, we employed the system GMM approach. Our findings underscore the significance of political connection and financial constraint as primary drivers of tax aggressiveness among listed non-financial firms in Ghana. The relationship between capital structure and tax aggressive behaviours is mixed. Our findings establish the ability of corporate governance measures – such as board size, board gender diversity and independent directors – to increase tax aggressive behaviours of listed non-financial corporations in Ghana. Policymakers could examine and evaluate the operability or enforcement of internal corporate governance practices among listed non-financial firms to limit the extent of tax aggressiveness which would, in turn, ensure high tax revenue mobilisation for the government. External governance measures such as institutional ownership and ownership structure policies could be evaluated to further ensure less tax aggressive behaviours from firms.

Why Not Pedal for the Planet? The Role of Perceived Norms for Driver Aggression as a Deterrent to Cycling

Cycling has many benefits for humans and the planet. This research investigates perceived norms and driver behavior toward cyclists as issues that may be useful for addressing reluctance to cycle. It connects perceived norms observed in the road context regarding aggressive driver behavior towards cyclists, and norms observed in workplaces regarding sustainability (perceived green psychological workplace climate) with driver aggressive behavior toward cyclists. Self-reported online survey responses from N = 426 Australian drivers were collected. Perceived norms regarding aggressive driver behavior toward cyclists were linked to drivers engaging more frequently in such behavior, but no such link was found for perceived green psychological workplace climate. However, perceived green psychological workplace climate moderated the link between perceived norms regarding aggressive driver behavior toward cyclists and drivers engaging in such behavior. When drivers perceived aggression toward cyclists to be common on the road, perceived green psychological workplace climate weakened the link between perceived norms regarding aggressive driver behavior towards cyclists and drivers engaging in such behavior. Findings reinforce the role of perceived road context norms regarding aggressive driver behavior toward cyclists for drivers engaging in such behavior. They illustrate that, while not directly linked, sustainability norms perceived in other contexts have a role in shaping car driver behavior towards cyclists. The study's findings suggest that interventions targeted at aggressive behavior toward cyclists in road contexts can focus on driver behavior norms and can be complemented by normative interventions in other settings to shape a key deterrent to cycling.

난폭운전 경향에 대한 연구: 오토바이 운전자와 승용차 운전자의 비교를 중심으로

난폭운전 경향에 대한 연구: 오토바이 운전자와 승용차 운전자의 비교를 중심으로

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 Comparison of Aggressive Driving Behavior between Pakistan and China Based on Driver Behavior Questionnaire

Aggressive Driving behavior plays a key role in road safety as it is important in road traffic accident prevention. The aim of this study was to investigate the aggressive driving behavior attitude towards road safety issues in Pakistan and china and the applicability of three-factor structure (aggressive violations, ordinary violations and errors) of both version of driver behavior questionnaire (DBQ). T-test was conducted to evaluate significant differences and it exposed that Chinese sample is more significant. Confirmatory factor analysis was tested and calculated on both samples. Chinese samples fit the CFA statistics models best as compared to Pakistani sample. Additionally, Pakistan drivers especially the younger drivers were mostly engaged in aggression and errors, while Chinese drivers appeared more disciplined and rule followers.

How Do Cognitive Interventions Impact Driver Aggressiveness in China?—A Driving Simulator Study

Aggressive driving behavior is one of the main reasons for traffic crashes in China. However, how cognitive interventions affect impulsive driving behavior is still unknown. In this study, a simulated drive was constructed to evaluate the influence of different cognitive interventions. In addition to speeding behavior in limit zones, speed when passing pedestrians, at intersections, and on the whole drive was adopted as a measure to evaluate aggressive driving behaviors. Forty-eight young drivers were recruited with monetary rewards. Compared to the control group, the penalty feedback intervention significantly reduced the mean speed in the 40 km/h zone, the 80 km/h zone, and when passing pedestrians. The combined feedback intervention significantly reduced the distance ratio of speeding in the 40 km/h and 80 km/h zones, as well as the mean speed at the intersection and on the whole drive. However, the interaction effects between the driving task and intervention method were not remarkably observed during aggressive driving behaviors. The findings from this study provide evidence that different cognitive interventions contribute distinctively to improving aggressive driving behaviors. These conclusive results have possible implications for the design of vehicle warning systems and traffic safety interventions.

Examining the Relationship between Experienced Workplace Incivility and Aggressive Driving Behaviors on the Work-to-Home Commute

Examining the Relationship between Experienced Workplace Incivility and Aggressive Driving Behaviors on the Work-to-Home Commute

The antecedents, regulation and maintenance of anger on the road: A qualitative investigation on the factors influencing driver anger and aggression

Aggressive driving behaviour (ADB) is an ongoing road safety issue that has proven difficult to deter. The purpose of this study was to: qualitatively investigate the antecedents of ADBs, document the self-regulatory processes and metacognitive beliefs associated with ADBs, and explore how metacognitive facets might influence the progression and expression of anger. An online sample of Queensland (Australia) motorists who had recently engaged in ADBs (N = 34) were interviewed and inductive thematic analysis was applied to the response data. The results showed that firstly, ADBs were primarily triggered by discrepancies in drivers’ mentality. Several perceptual, situational, internal and experiential factors were found to influence the likelihood and severity of the response to the triggering behaviour. Trait aggressive drivers tended to have lower perceptions of risk, and resistance to deterring events (e.g., fines; near misses). The self-regulation of ADBs appeared to be contingent on participants’ metacognitive awareness to recognise when their behaviour was being influenced by anger, and the ability to utilise appropriate cognitive-behavioural regulatory strategies when required. Following ADBs, participants reported different forms of evaluation, including anger, depressive, fearful, and reflective rumination styles, and these processes were perceived to influence future driving behaviours in different ways. However, trait aggressive drivers in particular tended to demonstrate a lack of evaluation following ADBs. The findings of this study may provide a framework for future research applying the concept of self-regulation to the topic of ADB and may also help inform road authorities of alternative and effective approaches for reducing driver aggression.

Stability of multi-population traffic flows

&lt;abstract&gt;&lt;p&gt;Traffic waves, known also as stop-and-go waves or phantom jams, appear naturally as traffic instabilities, also in confined environments as a ring-road. A multi-population traffic is studied on a ring-road, comprised of drivers with stable and unstable behavior. There exists a critical penetration rate of stable vehicles above which the system is stable, and under which the system is unstable. In the latter case, stop-and-go waves appear, provided enough cars are on the road. The critical penetration rate is explicitly computable, and, in reasonable situations, a small minority of aggressive drivers is enough to destabilize an otherwise very stable flow. This is a source of instability that a single population model would not be able to explain. Also, the multi-population system can be stable below the critical penetration rate if the number of cars is sufficiently small. Instability emerges as the number of cars increases, even if the traffic density remains the same (i.e., number of cars and road size increase similarly). This shows that small experiments could lead to deducing imprecise stability conditions.&lt;/p&gt;&lt;/abstract&gt;

Regulation requirements on automated driving systems in unsignalized intersections

A new EU regulation is being drafted to lay down the technical specifications concerning type-approval of Automated Driving System (ADSs). One of the requirements included in the regulation concerns the conditions for merging or crossing unsignalized intersections in the presence of oncoming privileged traffic. The requirements have been derived by studying the gap that human drivers usually accept in the same case. In particular, it has been found that a minimum separation in terms of Time to Collision (TTC) is required for the whole manoeuvre and it is demonstrated that only a percentage of the most aggressive drivers accept gaps that are smaller than what is required by the regulation.

Haptic Assistive Control With Learning-Based Driver Intent Recognition for Semi-Autonomous Vehicles

Semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. Further, the learning-based model has a 30.9% higher driver intent recognition accuracy than the finite state machine-based method. The holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer operation.