Aggressive Drivers Research Articles

Regulator of G protein signaling 12 drives inflammatory arthritis by activating synovial fibroblasts through MYCBP2/KIF2A signaling

Synovial fibroblasts are the active and aggressive drivers in the progression of arthritis, but the cellular and molecular mechanisms remain unknown. Here, our results showed that regulator of G protein signaling 12 (RGS12) maintained ciliogenesis in synovial fibroblasts, which is critical for the development of inflammatory arthritis. Deletion of RGS12 led to a significant decrease in ciliogenesis, adhesion, migration, and secretion of synovial fibroblasts. Mechanistically, RGS12 overexpression in synovial fibroblasts increased the length and number of cilia but decreased the protein level of kinesin family member 2A (KIF2A). The results of LC-MS analyses showed that RGS12 interacted with MYC binding protein 2 to enhance its ubiquitination activity, through which the KIF2A protein was degraded in synovial fibroblasts. Moreover, overexpression of KIF2A blocked the increases in cilia length and number. Mice with RGS12 deficiency or treatment of RGS12 shRNA nanoparticles significantly decreased the clinical score, paw swelling, synovitis, and cartilage destruction during inflammatory arthritis by inhibiting the activation of synovial fibroblasts. Therefore, this study provides evidence that RGS12 activates synovial fibroblasts' pathological function via promoting MCYBP2-mediated degradation of KIF2A and ciliogenesis. Our data suggest that RGS12 may be a potential drug target for the treatment of inflammatory arthritis.

Characterisation of driver longitudinal behaviour using an Unscented Kalman Filter

This paper presents a real-time driver characterisation algorithm that models the driver based on 10 parameters governing their control of vehicle longitudinal speed and acceleration. It utilises an open-loop longitudinal model in conjunction with an Unscented Kalman Filter (UKF). The algorithm can operate in real-time using velocity and acceleration measurements, in addition to a-priori knowledge of the path curvature and road features such as speed limit locations. The UKF also enables a system identification process to estimate average response of each driver. These identified parameters are subsequently fitted to independent measures of fuel economy and safety to demonstrate possible uses for the characterisations. In addition to the comprehensive characterisation of the drivers’ behaviour the work is also novel in its unique approach to identifying time-based parameters within a time-varying dynamic system (UKF). Possible applications include insurance black box assessments of driver aggressiveness and safety, and in autonomous driving algorithms by providing a model to closely replicate the specific driving styles of various users.

Analysis of construction area’s impacts on traffic flow: A case study on Hangzhou Bay Bridge in China

Modeling impacts of the construction area on the mixed traffic flow would be a challenge. It needs to figure out the traffic regulations near the construction area, which vehicles must comply with. Meanwhile it requires depicting the different driving behaviors of cars and trucks when they run into the construction area. To deal with these problems, we present a three-lane cellular automaton traffic model with a construction area, taking the traffic flow on Hangzhou Bay Bridge with the third lane closed as a case study. The different longitudinal position updating rules and the lateral lane-changing rules are proposed for cars and trucks respectively, which are driven by aggressive drivers and cautious drivers. The simulation results show that the traffic flow will reach the maximum value when the length of the work zone, i.e., the main part of the construction area, is appropriate. On the other hand, the construction area has a great negative impact on the traffic flow when the injection rate is high, and cause the serious traffic congestions. In order to optimize the traffic flow, the variation tendency of the total traffic flow and the delay time with the increasing length of the work zone and the injection rate are analyzed. And the recommended values of the work zone’s length under different injection rates are obtained. In addition, according to the variation trend of the merging rate with the increasing length of the merging area and the injection rate, the recommended values of the merging area’s length under different injection rate are obtained. The results are meaningful and expected to be applied in the traffic management during the period of highway construction.

CLASSIFICATION AND MODELING OF DRIVER BEHAVIOR DURING YELLOW INTERVALS AT INTERSECTIONS

Abstract. The violation of traffic rules is, nowadays, the most important cause of accidents. Passing an intersection or a red light can be fatal for a driver and lead to serious damage. In fact, when the driver encounters a signal change from green to yellow, he or she is required to make a decision to stop or to go based on many factors. Making the wrong decision will result in a red-light violation or an abrupt stop at the intersection. Researchers typically focus on the connection between driving behavior and decision-making because of its importance in controlling aggressive drivers’ behavior. This work aims to compare the potential of machine learning techniques to classify driver behavior at intersections and follows a data preparation process to expect interesting performance results. A comparative study was therefore conducted to explore the various data source and algorithms employed to classify driver behaviors at intersections and to address the most important techniques used. Two experiments were also developed in this paper. The first experience attempts to classify driver behavior in intersections into (1) stopping and (2) going at intersections. The second experience was based on stopping observations when approaching intersections. We classified these drivers into two categories: those who stop beyond the line (1) are considered dangerous or unsafe stops, and those who stop before the line (2) are considered safe stops. As a result, XBboost archive the best performance with 92.19% of accuracy and 94.38% of precision in the first experience and RF gives the best performance in the second experience with an accuracy of 99.38%.

Kaitan Moral Disengagement dan Aggressive Driving Behavior : Tinjauan Pada Remaja Pengendara Sepeda motor

One of the factors of high traffic accidents is the growing rate of vehicles. In Indonesia, traffic accidents that occur are dominated by two-wheeled vehicles, namely motorcycles and often involve young drivers. One of the causes of adolescent involvement in accidents is aggressive driving behavior which can be influenced by moral disengagement. This quantitative study aims to determine the role of moral disengagement on aggressive driving behavior in adolescent motorcyclists. The hypothesis of this research is that moral disengagement plays a role to aggressive driving behavior on adolescent motorcyclist. This research uses purposive sampling as a sampling technique. The respondents range from 14-19 year old motorcyclists in Palembang, amounting to about 150 respondents. The instrument used is the Aggressive Driving Behavior Scale (ADBS) by Houston et al. (2003) and the Moral Disengagement Scale by Bandura et al. (1996), both modified. Simple regression technique was used to analyze the research data and with the help of IBM SPSS 22. Based on the results of the regression analysis, the R square value between moral disengagement and aggressive driving behavior is 0.107, the F value is 17.682, and the significance value is 0.000 (p<0.005). This shows that moral disengagement has a significant role in aggressive driving behavior. Thus, the hypothesis proposed by this study can be accepted.

Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following

Driving aggression is a major concern during car-following situations as it is closely associated with collision risk and crash severity. Despite the tendency of reckless driving actions and increased crash risk with the vehicle ahead, aggressive driver behavior remains unexplored. The present study aimed to investigate the effects of aggressive driver behavior (three driver categories: aggressive, moderately aggressive, and non-aggressive drivers) and drivers’ tendency to aggressive stimuli (due to lead vehicle (vehicle moving ahead) reckless driving) on car-following behavior. A sample of fifty-eight Indian drivers participated in this study. All the experiments were conducted using a driving simulator. The simulator design includes a lead vehicle dynamic maneuver with rapid accelerations and decelerations (at 1 m/s2, 1.5 m/s2, and 2 m/s2). Three longitudinal performance measures including speed variability (SPV) from LV, speed recovery time (SRCT), and time spent tailgating, were analyzed during LV acceleration stage using generalized linear models (GLM). Further, to assess collision avoidance behavior and crash risk, deceleration adjusting time (DAT) was analyzed during the LV sudden deceleration stage using Weibull Accelerated Failure Time (AFT) analysis. The findings indicate that due to their tendency to risk-raking behavior, aggressive drivers decreased their SPV and SRCT by 25 % and 18 %, respectively. The time spent tailgating increased by 107 % for aggressive drivers. The survival probabilities were decreased by 23.77 % for aggressive drivers at 3 sec DAT. The findings of the study can be used in car-following models to enhance the model performance, improve the reliability of simulation tools, and design interventions to improve safety (in automated driving).

Recognition of Trip-Based Aggressive Driving: A System Integrated With Gaussian Mixture Model Structured of Factor-Analysis, and Hierarchical Clustering

Recognition of aggressive driving behavior helps future research and practices in Intelligent Transport Systems. This paper tries to briefly explain the concepts related to aggressive driving behavior and introduces a system integrated with a novel machine learning algorithm for the recognition of trip-based aggressive driving behavior. The algorithm is a Gaussian Mixture Model (GMM) structured with Factor Analysis (FA), and Hierarchical Clustering (HC): common factors were extracted using FA, which is further applied to HC and GMM in the recognition of trip-based aggressive driving. The system is applied in a case study using data from the Shanghai Naturalistic Driving Study, for simulating data collection using the Advanced Driving Assistance System (ADAS) system in a real-traffic situation. Three behavior types (cautious, regular, and aggressive driving) were successfully clustered. For validity, the real aggressive driving behavior records were extracted based on the video, and the proposed system was compared with existing recognition methods. Results indicate that the accuracy of aggressive driving recognition of the system is higher than others (accuracy = 87%). This paper provides a reference in defining and determining aggressive driving, and a robust system for aggressive driving behavior recognition along with the trained algorithm, which can be used in real-world applications for improving driving safety with the applications in ADAS systems, auto-insurance industry.

The relationship of bicycle use and aggressive driving behaviour: a latent variable model approach

ABSTRACT Aggressive driving behaviour has been related to the occurrence of risky situations and crashes. The current paper focuses on investigating individual characteristics as determinants of aggressive driving behaviour, with a specific focus on bicycle use. The analysis is based on a questionnaire survey conducted in a number of parking sites in the city centre of Thessaloniki, Greece. Aggressive driving was approximated as a latent variable with reported lane-changing behaviour, use of emergency lane on motorways and maximum speed in urban areas as its indicators. Sociodemographic characteristics and bicycle use were used as explanatory variables. The results showed that increase of aggressiveness had a positive relation to all indicators. Female respondents and older drivers were less likely to report aggressive behaviour. Also, bicycle users were less likely to be aggressive drivers compared to car drivers only. This finding shows that cycling could reduce aggressive behaviour and improve road safety.

A Fuzzy Full Velocity Difference Model Based on Driver’s Perception Characteristics

The perception characteristics of drivers greatly vary with the status of traffic flow. To designate the vehicle trajectory more accurately, a driver’s perception headway coefficient is introduced, and a fuzzy full velocity difference (FVD) model is proposed in this paper. Consistent with the control theory, the stability conditions of the improved model are derived. Through the fuzzy control method, the input and output items of the control rule are constructed, respectively. The genetic algorithm is operated to calibrate the model parameters of timid and aggressive drivers. Eventually, the effectiveness of the model is verified by simulations. The research results show that, with the decrease of driver’s perception headway coefficient, the stability range of the traffic flow gradually increases, which is beneficial. Additionally, the average root means square percentage errors (RMSPE) values of the full velocity difference (FVD) model, the intelligent driver model (IDM), and the proposed model are, respectively, 0.312, 0.308, and 0.213. Compared with IDM and FVD models, the proposed model can accurately describe the local velocity variations and determine the car-following behavior of the human driving vehicle better.

PARP3 supervises G9a-mediated repression of adhesion and hypoxia-responsive genes in glioblastoma cells

In breast cancer, Poly(ADP-ribose) polymerase 3 (PARP3) has been identified as a key driver of tumor aggressiveness exemplifying its selective inhibition as a promising surrogate for clinical activity onto difficult-to-treat cancers. Here we explored the role of PARP3 in the oncogenicity of glioblastoma, the most aggressive type of brain cancer. The absence of PARP3 did not alter cell proliferation nor the in vivo tumorigenic potential of glioblastoma cells. We identified a physical and functional interaction of PARP3 with the histone H3 lysine 9 methyltransferase G9a. We show that PARP3 helps to adjust G9a-dependent repression of the adhesion genes Nfasc and Parvb and the hypoxia-responsive genes Hif-2α, Runx3, Mlh1, Ndrg1, Ndrg2 and Ndrg4. Specifically for Nfasc, Parvb and Ndrg4, PARP3/G9a cooperate for an adjusted establishment of the repressive mark H3K9me2. While examining the functional consequence in cell response to hypoxia, we discovered that PARP3 acts to maintain the cytoskeletal microtubule stability. As a result, the absence of PARP3 markedly increases the sensitivity of glioblastoma cells to microtubule-destabilizing agents providing a new therapeutic avenue for PARP3 inhibition in brain cancer therapy.

The Role of Trait Mindfulness in Aggressive Driving Behavior

Aggressive driving behavior is regarded as a social dysfunction that poses a major risk to traffic safety. Previous research has indicated trait mindfulness as a protective factor for maladaptive thoughts and driver anger. The current study was designed to examine the mediating role of trait driver aggressiveness on the relationship between trait mindfulness and propensity for aggressive driving behavior. A sample of 122 drivers responded to a series of online questionnaires that assessed trait mindfulness, trait driver aggressiveness, and propensity for aggressive driving behavior. Results indicated that trait mindfulness was a significant predictor of trait driver aggressiveness. Similarly, trait mindfulness had also a moderating effect on the relationship between propensity for aggressive driving and trait driver aggressiveness. This indicated that those with higher trait mindfulness scores and lower trait driver aggressiveness scores were less likely to engage in aggressive driving behavior. Both theoretical and practical implications are discussed, and directions for future research are presented.

The Effect of ADHD Diagnosis, Roadway Environment, and Traffic Aggressiveness on Aggressive Driving Behavior

Aggressive driving behavior poses a serious risk to public safety worldwide. Aggressive driving is described as a socially dysfunctional behaviors marked by unsafe driving maneuvers that pose as a risk to driver safety. Sixty-seven drivers with previous driving experience participated in this study. Participants were required to complete a series of driving behavior questionnaires and simulated driving scenarios. Furthermore, an electrocardiogram (ECG) was used to collect heart rate variability (HRV) scores as a measure of mental workload. It was hypothesized that attention-deficit hyperactivity disorder (ADHD) diagnosis, roadway environment, and traffic aggressiveness would have an effect on driving performance and mental workload. Results indicated that roadway environment and traffic aggressiveness had a significant effect on driving performance. Results from the HRV analyses showed that ADHD participants had significantly higher HRV scores than their non-ADHD counterparts. Collectively, these findings highlight the utility of HRV measure for evaluating mental workload among ADHD drivers. Our findings have practical implications for driver assessment, roadway design, and traffic safety.

Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles

The research of early warning and control strategy considering driving styles during lane changes is a hotspot in the field of automatic driving. However, many lane-changing studies only emphasize the warning analysis when the following vehicle is faster than the subject vehicle, while neglecting the potential risk when the subject vehicle is faster than the following vehicle in the adjacent lane during lane changes. To study the lane-changing characteristics of drivers considering driving styles and to establish a personalized lane-changing warning model under different relative speed conditions, fifty participants (three females and forty-seven males) were recruited to carry out a real road driving test. A novel Gaussian mixture model with the results of k-means clustering was established to classify driving styles based on two-dimensional variables: average time gap and average minimum time to collision. The clustering result was then verified. In addition, by analyzing the relationship between the subject vehicle and the following vehicle in the adjacent lane during lane changes, a lane-changing warning model considering driving styles under different relative speed conditions was established. Results show that the clustering algorithm proposed in this paper has high separability between samples, achieving a much softer clustering result that can provide a reference for the parameter setting of the personalized driver assistance system. Furthermore, the overall recognition accuracy of the hazardous lane-changing behaviors improved after drivers were classified into different driving styles. The established lane-changing warning model has a better recognition performance for aggressive drivers when compared with the other two driver types. The results provide a basis for the algorithm design of the intelligent lane-changing warning system and can improve the user acceptance of an advanced driver assistance system for self-driving vehicles.

The Analysis of Classification and Spatiotemporal Distribution Characteristics of Ride-Hailing Driver's Driving Style: A Case Study in China.

Monitoring the driving styles of ride-hailing drivers is helpful for providing targeted training for drivers and improving the safety of the service. However, previous studies have lacked analyses of the temporal variation as well as spatial variation characteristics of driving styles. Understanding the variations can also help authorities formulate driver management policies. In this study, trajectory data are used to analyze driving styles in various temporal and spatial scenarios involving 34,167 drivers. The k-means method is used to cluster sample drivers. In terms of driving style time-varying, we found that only 31.79% of drivers could maintain a stable driving style throughout the day. Spatially, we divided the research area into two parts, namely, road segments and intersections, to analyze the spatial driving characteristics of drivers with different styles. The speed distribution, the acceleration and deceleration distributions are analyzed, results indicated that aggressive drivers display more aggressive driving styles in road segments, and conservative drivers exhibit more conservative driving styles at intersections. The findings of this study provide an understanding of temporal and spatial driving behavior factors for ride-hailing drivers and offer valuable contributions to ride-hailing driver training and road safety management.

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.

Ca2+ Signalling and Hypoxia/Acidic Tumour Microenvironment Interplay in Tumour Progression.

Solid tumours are characterised by an altered microenvironment (TME) from the physicochemical point of view, displaying a highly hypoxic and acidic interstitial fluid. Hypoxia results from uncontrolled proliferation, aberrant vascularization and altered cancer cell metabolism. Tumour cellular apparatus adapts to hypoxia by altering its metabolism and behaviour, increasing its migratory and metastatic abilities by the acquisition of a mesenchymal phenotype and selection of aggressive tumour cell clones. Extracellular acidosis is considered a cancer hallmark, acting as a driver of cancer aggressiveness by promoting tumour metastasis and chemoresistance via the selection of more aggressive cell phenotypes, although the underlying mechanism is still not clear. In this context, Ca2+ channels represent good target candidates due to their ability to integrate signals from the TME. Ca2+ channels are pH and hypoxia sensors and alterations in Ca2+ homeostasis in cancer progression and vascularization have been extensively reported. In the present review, we present an up-to-date and critical view on Ca2+ permeable ion channels, with a major focus on TRPs, SOCs and PIEZO channels, which are modulated by tumour hypoxia and acidosis, as well as the consequent role of the altered Ca2+ signals on cancer progression hallmarks. We believe that a deeper comprehension of the Ca2+ signalling and acidic pH/hypoxia interplay will break new ground for the discovery of alternative and attractive therapeutic targets.

Risk-Aware Optimal Control for Automated Overtaking With Safety Guarantees

This article proposes a solution to the overtaking control problem where an automated vehicle tries to overtake another vehicle with uncertain motion. Our solution allows the automated vehicle to robustly overtake a human-driven vehicle under certain assumptions. Uncertainty in the predicted motion makes the automated overtaking problem hard to solve due to feasibility issues that arise from the fact that the overtaken vehicle (e.g., a vehicle driven by an aggressive driver) may accelerate to prevent the overtaking maneuver. To counteract them, we introduce the weak assumption that the predicted velocity of the overtaken vehicle respects a supermartingale, meaning that its velocity is not increasing in expectation during the maneuver. We show that this formulation presents a natural notion of risk. Based on the martingale assumption, we perform a risk-aware reachability analysis by analytically characterizing the predicted collision probability. Then, we design a risk-aware optimal overtaking algorithm with guaranteed levels of collision avoidance. Finally, we illustrate the effectiveness of the proposed algorithm with a simulated example.

A Delay Compensation Framework for Connected Testbeds

This work considers connected testbeds, i.e., engineering testbeds that are remotely accessible over a network, and focuses on the challenge of achieving a high-fidelity closed-loop integration despite the network delays. To address this challenge, a model-free framework is presented. In previous work, a model-free delay compensation framework with a single design parameter was developed. However, having only one design parameter could limit tuning flexibility and thus delay compensation performance. Furthermore, this framework has not yet been validated on an actual connected testbed. To address these two shortcomings, in this article, this delay compensation framework is first expanded into a two-parameter form, and its open-loop stability and performance are studied for constant delays. The predictor stability criterion is established in terms of the two design parameters and the constant network delay. To evaluate the transient delay compensation performance of this framework and understand its advantages and limitations, simulation case studies are conducted on a connected engine testbed representing a medium-duty diesel pickup truck. These studies include two testbed architectures, constant and variable delays, two drive cycles, two levels of driver aggressiveness, and a comparison against wave transformation as a state-of-the-art benchmark. Finally, the framework is tested experimentally on a physical realization of the same connected testbed. The results validate the robustness of the delay compensation performance of the framework under dynamic operations, and show up to 30% higher integration fidelity due to the additional design degree of freedom introduced through the two-parameter form.

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.

Monocarboxylate Transporter 4 in Cancer-Associated Fibroblasts Is a Driver of Aggressiveness in Aerodigestive Tract Cancers.

The most common cancers of the aerodigestive tract (ADT) are non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC). The tumor stroma plays an important role in ADT cancer development and progression, and contributes to the metabolic heterogeneity of tumors. Cancer-associated fibroblasts (CAFs) are the most abundant cell type in the tumor stroma of ADT cancers and exert pro-tumorigenic functions. Metabolically, glycolytic CAFs support the energy needs of oxidative (OXPHOS) carcinoma cells. Upregulation of the monocarboxylate transporter 4 (MCT4) and downregulation of isocitrate dehydrogenase 3α (IDH3α) are markers of glycolysis in CAFs, and upregulation of the monocarboxylate transporter 1 (MCT1) and the translocase of the outer mitochondrial membrane 20 (TOMM20) are markers of OXPHOS in carcinoma cells. It is unknown if glycolytic metabolism in CAFs is a driver of ADT cancer aggressiveness. In this study, co-cultures in vitro and co-injections in mice of ADT carcinoma cells with fibroblasts were used as experimental models to study the effects of fibroblasts on metabolic compartmentalization, oxidative stress, carcinoma cell proliferation and apoptosis, and overall tumor growth. Glycolytic metabolism in fibroblasts was modulated using the HIF-1α inhibitor BAY 87-2243, the antioxidant N-acetyl cysteine, and genetic depletion of MCT4. We found that ADT human tumors express markers of metabolic compartmentalization and that co-culture models of ADT cancers recapitulate human metabolic compartmentalization, have high levels of oxidative stress, and promote carcinoma cell proliferation and survival. In these models, BAY 87-2243 rescues IDH3α expression and NAC reduces MCT4 expression in fibroblasts, and these treatments decrease ADT carcinoma cell proliferation and increase cell death. Genetic depletion of fibroblast MCT4 decreases proliferation and survival of ADT carcinoma cells in co-culture. Moreover, co-injection of ADT carcinoma cells with fibroblasts lacking MCT4 reduces tumor growth and decreases the expression of markers of metabolic compartmentalization in tumors. In conclusion, metabolic compartmentalization with high expression of MCT4 in CAFs drives aggressiveness in ADT cancers.