Investigating the feasibility of vehicle telemetry data as a means of predicting driver workload

Driving is a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, we present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. We perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem

Data mining for vehicle telemetry

This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labelling problems: Road Type (A, B, C and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, namely, signal selection, feature extraction, and feature selection. The selection methods used include Principal Components Analysis (PCA) and Mutual Information (MI), which are used to determine the relevance and redundancy of extracted features, and are performed in various combinations. Finally, as there is an inherent bias towards certain road and carriageway labellings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension heigh

Warwick-JLR driver monitoring dataset (DMD) : statistics and early findings

Driving is a safety critical task that requires a high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to monitor workload online using readily available and robust sensors accessible via the vehicle's Controller Area Network (CAN). In this paper, we present details of the Warwick-JLR Driver Monitoring Dataset (DMD) collected for this purpose, and to announce its publication for driver monitoring research. The collection protocol is briefly introduced, followed by statistical analysis of the dataset to describe its structure. Finally, the public release of the dataset, for use in both driver monitoring and data mining research, is announced

Data mining for vehicle telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height

Mobilisation of arsenic from bauxite residue (red mud) affected soils: effect of pH and redox conditions

The tailings dam breach at the Ajka alumina plant, western Hungary in 2010 introduced ~1 million m3 of red mud suspension into the surrounding area. Red mud (fine fraction bauxite residue) has a characteristically alkaline pH and contains several potentially toxic elements, including arsenic. Aerobic and anaerobic batch experiments were prepared using soils from near Ajka in order to investigate the effects of red mud addition on soil biogeochemistry and arsenic mobility in soil–water experiments representative of land affected by the red mud spill. XAS analysis showed that As was present in the red mud as As(V) in the form of arsenate. The remobilisation of red mud associated arsenate was highly pH dependent and the addition of phosphate to red mud suspensions greatly enhanced As release to solution. In aerobic batch experiments, where red mud was mixed with soils, As release to solution was highly dependent on pH. Carbonation of these alkaline solutions by dissolution of atmospheric CO2 reduced pH, which resulted in a decrease of aqueous As concentrations over time. However, this did not result in complete removal of aqueous As in any of the experiments. Carbonation did not occur in anaerobic experiments and pH remained high. Aqueous As concentrations initially increased in all the anaerobic red mud amended experiments, and then remained relatively constant as the systems became more reducing, both XANES and HPLC–ICP-MS showed that no As reduction processes occurred and that only As(V) species were present. These experiments show that there is the potential for increased As mobility in soil–water systems affected by red mud addition under both aerobic and anaerobic conditions

Road type classification through data mining

In this paper we investigate data mining approaches to road type classification based on CAN (controller area network) bus data collected from vehicles on UK roads. We consider three related classification problems: road type (A, B, C and Motorway), signage (None, White, Green and Blue) and carriageway type (Single or Double). Knowledge of these classifications has a number of uses, including tuning the engine and adapting the user interface according to the situation. Furthermore, the current road type and surrounding area gives an indication of the driver's workload. In a residential area the driver is likely to be overloaded, while they may be under stimulated on a highway. Several data mining and temporal analysis techniques are investigated, along with selected ensemble classifiers and initial attempts to deal with a class imbalance present in the data. We find that the Random Forest ensemble algorithm has the best performance, with an AUC of 0.89 when used with a wavelet-Gaussian summary of the previous 2.5 seconds of speed and steering wheel angle recordings. We show that this technique is at least as good as a model-based solution that was manually created using domain expertise

Leaching of copper and nickel in soil-water systems contaminated by bauxite residue (red mud) from Ajka, Hungary: the importance of soil organic matter

Red mud is a highly alkaline (pH >12) waste product from bauxite ore processing. The red mud spill at Ajka, Hungary, in 2010 released 1 million m3 of caustic red mud into the surrounding area with devastating results. Aerobic and anaerobic batch experiments and solid phase extraction techniques were used to assess the impact of red mud addition on the mobility of Cu and Ni in soils from near the Ajka spill site. Red mud addition increases aqueous dissolved organic carbon (DOC) concentrations due to soil alkalisation, and this led to increased mobility of Cu and Ni complexed to organic matter. With Ajka soils, more Cu was mobilised by contact with red mud than Ni, despite a higher overall Ni concentration in the solid phase. This is most probably because Cu has a higher affinity to form complexes with organic matter than Ni. In aerobic experiments, contact with the atmosphere reduced soil pH via carbonation reactions, and this reduced organic matter dissolution and thereby lowered Cu/Ni mobility. These data show that the mixing of red mud into organic rich soils is an area of concern, as there is a potential to mobilise Cu and Ni as organically bound complexes, via soil alkalisation. This could be especially problematic in locations where anaerobic conditions can prevail, such as wetland areas contaminated by the spill