Recent technological innovations and the need for increased safety on the world's roads have led to the introduction of In- Vehicle Information Systems (IVIS). These systems will provide navigation and advisory information to drivers while they are driving. One aspect of these systems, In-vehicle Signing Information Systems (ISIS), would provide the warning, regulatory, and advisory information that is currently found on road signs. These systems may be of particular benefit when external elements such as rain, snow, or night driving reduce or eliminate the opportunity for drivers to detect road signs. This study attempts to determine what benefits, if any, are realized by drivers using this system. Fifty-eight drivers operated an instrumented Oldsmobile Aurora under eight conditions. The eight conditions consisted of a daylight-clear weather-ISIS condition, a daylight-clear weather-No ISIS condition, a daylight-rain-ISIS condition, a daylight-rain-No ISIS condition, a night-clear weather-ISIS condition, a night-clear weather-No ISIS condition, a night-rain-ISIS condition, and a night-rain-No ISIS condition. Younger drivers (18-30 years old) and older drivers (65 years or older) took part in this study. Three measures of driver performance were collected along with subjective preference data. Each measure was evaluated in order to determine what impact, if any, weather, time of day, age, and ISIS use had on performance. Subjective data was evaluated to determine driver preference and acceptance of the ISIS display. The results indicated that use of the ISIS display led to reduced speeds and greater reaction distances for all drivers. Evidence was found that seems to indicate that older drivers may receive a greater benefit in complex, unfamiliar, or low visibility situations. Evidence was also found that indicates that all drivers may receive a greater benefit at night for the complex or unfamiliar situations. Subjectively, the majority of the drivers indicated that the ISIS display made them more aware of road sign information. / Master of Science

Obstructive sleep apnoea (OSA) is characterised by repetitive reductions or pauses in breathing during sleep due to upper airway narrowing or closure. Due to disruption to normal sleep patterns, many patients with OSA suffer from increased daytime sleepiness. Epidemiological studies have established a link between OSA and driver fatigue and accidents, generally showing a two to seven times increased risk of road traffic accidents in non-commercial drivers with OSA. There is emerging evidence that commercial drivers have a higher prevalence of OSA than the general population, being predominately male, middle-aged and overweight, three important risk factors for OSA. However, little is known about the relationship between OSA and driver sleepiness in commercial drivers, whether road accidents are increased in commercial drivers with OSA, and whether OSA interacts with other fatigue promoting factors, such as sleep deprivation, to further escalate road accident risk. One thousand randomly selected commercial drivers were surveyed in the field. In addition, 61 randomly selected NSW commercial drivers had in hospital sleep studies and daytime performance testing, including a PC based driving simulator task. The prevalence of OSA, defined as Respiratory Disturbance Index (RDI) < 10, was approximately 50% in NSW commercial drivers. Approximately one quarter of the drivers reported pathological daytime sleepiness, and 12-14% had both OSA and pathological daytime sleepiness. A diagnosis of OSA was the most important factor predicting excessive daytime sleepiness in these drivers: OSA was more important than 15 other work-related, lifestyle and medical factors that could be expected to promote, or be associated with, daytime sleepiness. Drivers with sleep apnoea syndrome (both OSA and pathological daytime sleepiness) had an increased driving accident risk, using driving simulator and daytime performance testing as proxy measures for accident risk. These results demonstrate the importance of OSA as a cause of driver fatigue in commercial drivers and suggest that all commercial drivers should be screened for the presence of sleep apnoea syndrome in order to potentially reduce road accident risk through treatment. A separate, but related body of work examined the combined effects of mild OSA and other fatigue promoting factors (sleep deprivation and circadian influences) on driving performance. Twenty nine subjects, consisting of a group with mild OSA and a group of non-OSA controls, were tested on several occasions throughout the night and day using an intensive performance battery, under both baseline conditions and after a period of 36 hours of total sleep deprivation. The results suggest that drivers with mild OSA are not different to the control group in their response to sleep deprivation or time of day influences. However, the subjects with mild OSA were less aware of their impairment due to sleep deprivation, which is of concern if drivers with OSA are relying on their subjective awareness of fatigue to make decisions about when to stop driving. A final perspective on OSA and driver fatigue is provided through a clinical case series of seven fall-asleep fatality associated MVA�s associated with unrecognised or under-treated sleep disorders. As well as demonstrating the day to day potential for devastating road accidents due, at least in part, to un-recognised or untreated sleep disorders, these cases also serve to highlight some of the current medico-legal controversies and difficulties in this area of driver fatigue. In conclusion, this body of work has provided novel information about the epidemiology and implications of OSA in commercial drivers, and about how OSA interacts with other fatigue promoting factors. Finally, it has explored some of the medico-legal issues that relate to sleep disorders and driver fatigue. As well as providing much needed information in the area of driver fatigue, at the same time this work raises many more questions and suggests areas of future research. For instance, such research should examine the relationship between objective accident rates and OSA/sleep apnoea syndrome in commercial drivers, the interaction between mild sleep apnoea syndrome and other fatigue risk factors, and driver perception of sleepiness prior to sleep onset in drivers with sleep disorders.

Obstructive sleep apnoea (OSA) is characterised by repetitive reductions or pauses in breathing during sleep due to upper airway narrowing or closure. Due to disruption to normal sleep patterns, many patients with OSA suffer from increased daytime sleepiness. Epidemiological studies have established a link between OSA and driver fatigue and accidents, generally showing a two to seven times increased risk of road traffic accidents in non-commercial drivers with OSA. There is emerging evidence that commercial drivers have a higher prevalence of OSA than the general population, being predominately male, middle-aged and overweight, three important risk factors for OSA. However, little is known about the relationship between OSA and driver sleepiness in commercial drivers, whether road accidents are increased in commercial drivers with OSA, and whether OSA interacts with other fatigue promoting factors, such as sleep deprivation, to further escalate road accident risk. One thousand randomly selected commercial drivers were surveyed in the field. In addition, 61 randomly selected NSW commercial drivers had in hospital sleep studies and daytime performance testing, including a PC based driving simulator task. The prevalence of OSA, defined as Respiratory Disturbance Index (RDI) < 10, was approximately 50% in NSW commercial drivers. Approximately one quarter of the drivers reported pathological daytime sleepiness, and 12-14% had both OSA and pathological daytime sleepiness. A diagnosis of OSA was the most important factor predicting excessive daytime sleepiness in these drivers: OSA was more important than 15 other work-related, lifestyle and medical factors that could be expected to promote, or be associated with, daytime sleepiness. Drivers with sleep apnoea syndrome (both OSA and pathological daytime sleepiness) had an increased driving accident risk, using driving simulator and daytime performance testing as proxy measures for accident risk. These results demonstrate the importance of OSA as a cause of driver fatigue in commercial drivers and suggest that all commercial drivers should be screened for the presence of sleep apnoea syndrome in order to potentially reduce road accident risk through treatment. A separate, but related body of work examined the combined effects of mild OSA and other fatigue promoting factors (sleep deprivation and circadian influences) on driving performance. Twenty nine subjects, consisting of a group with mild OSA and a group of non-OSA controls, were tested on several occasions throughout the night and day using an intensive performance battery, under both baseline conditions and after a period of 36 hours of total sleep deprivation. The results suggest that drivers with mild OSA are not different to the control group in their response to sleep deprivation or time of day influences. However, the subjects with mild OSA were less aware of their impairment due to sleep deprivation, which is of concern if drivers with OSA are relying on their subjective awareness of fatigue to make decisions about when to stop driving. A final perspective on OSA and driver fatigue is provided through a clinical case series of seven fall-asleep fatality associated MVA�s associated with unrecognised or under-treated sleep disorders. As well as demonstrating the day to day potential for devastating road accidents due, at least in part, to un-recognised or untreated sleep disorders, these cases also serve to highlight some of the current medico-legal controversies and difficulties in this area of driver fatigue. In conclusion, this body of work has provided novel information about the epidemiology and implications of OSA in commercial drivers, and about how OSA interacts with other fatigue promoting factors. Finally, it has explored some of the medico-legal issues that relate to sleep disorders and driver fatigue. As well as providing much needed information in the area of driver fatigue, at the same time this work raises many more questions and suggests areas of future research. For instance, such research should examine the relationship between objective accident rates and OSA/sleep apnoea syndrome in commercial drivers, the interaction between mild sleep apnoea syndrome and other fatigue risk factors, and driver perception of sleepiness prior to sleep onset in drivers with sleep disorders.

This dissertation presents two studies examining the interaction between workload history and driver mental workload. The first experiment focuses on testing for the presence of a hysteresis effect in the driving task. The second experiment examines the proposition that cueing impending periods of higher task demand can reduce the impact of any such potential hysteresis effects. Thirty-two licensed drivers served as participants and all served in both studies. Using the directions provided by a Heads-Up-Display navigation system, participants followed a pre-set route in the simulated environment. At specified points within the drive, the navigation system would purposefully fail which required drivers to relay a ten digit alphanumeric error code to a remote operator in order to reset the system. Results indicated that this increase in task demand from the navigation system's failure leads to a significant increase in perceived mental workload as compared to pre-failure periods. This increase in driver mental workload was not significantly reduced by the time the drive ended, indicating the presence of a hysteresis effect. In the second experiment, the navigation system provided a completely reliable visual warning before failure. Results indicate that cueing had neither an effect on perceived mental workload, nor any ameliorating effect on the hysteretic type effect seen in mental workload recovery. The conclusion of these findings being that the overall safety and efficiency of the surface transportation system would likely improve by designs which accommodate the periods immediately following a reduction in stress. Whether from leaving high demand areas such as work zones or in the period immediately after using a in-car information device such as a GPS or a cell phone, these post-high workload periods are associated with increased variability in driver inputs and levels of mental workload.

No description available.

Current return-to-drive recommendations for patients following rotator cuff repair (RCR) surgery are not uniform due to a lack of empirical evidence relating driving safety and time-after-surgery. To address the limitations of previous work, Badger et al. (2022) evaluated, on public roads, the driving fitness of patients prior to RCR and at multiple post-operative timepoints. The goal of the Badger, et al. study was to make evidence-based return-to-drive recommendations in an environment with higher fidelity than that of a simulator and not subject to biases inherent to surveys. Badger et al., however, do not fully investigate the driving practices exhibited by subjects, overlooking the potential presence of compensatory driver behaviors. Further investigation of these behaviors through observation of direct driving techniques and practices over time can specifically answer how drivers may modify their behaviors to address a perceived state of impairment. Additionally, the degree of success in vehicle operation by comparing an ideal turn to the path taken by the driver allows for a level of quantification of the effectiveness of the compensatory techniques. Moreover, driver trajectories inferred from the vehicle Controller Area Network (CAN) metrics and from global positioning system (GPS) coordinates are contrasted with the ideal turn to assess minimum requirements for future sensors that are used to make these trajectory comparisons. This investigation leverages pre-existing data collected by the Virginia Tech Transportation Institute (VTTI) and Carilion Clinic as used in the analysis performed by Badger et al. (2022). RCR patients (n=27) executed the same prescribed driving maneuvers and drove the same route in a preoperative state and at 2-, 4-, 6-, and 12-weeks post operation. Behavioral data were annotated to extract key characteristics of interest and related them to relevant vehicle sensor readings. To construct vehicle paths, data was obtained from the on-board data acquisition system (DAS). Behavioral metrics considered the use of ipsilateral vehicle controls, performance of non-primary vehicle tasks, and steering techniques utilized to assess the impact of mobility restrictions due to sling use. Sling use was found to be a significant factor in use of the non-ipsilateral hand associated with the operative extremity (i.e., operative hand) on vehicle functions and, in particular, difficulty with the gear shifting control. Additionally, when considering the performance of non-primary vehicle tasks as assessed through a prescribed visor manipulation, sling use was not a significant factor for the task duration or completion of the task in a fluid motion. Sling use was, however, significant with respect to operative hand position prior to the completion of the visor manipulation: the operative hand was often not on the steering wheel prior to the visor maneuver. In addition, the operative hand was never used to manipulate the visor when the sling was worn. One-handed steering was also more frequent with the presence of the sling. Further behavioral analysis assessed the presence of compensatory behavior exhibited by subjects during periods in which impairment was perceived. Perceived impairment was observed as a function of the different experimental timepoints. Findings indicated a significant decrease in the lateral vehicle jerk during post-operative weeks 6 and 12. Significant differences, however, were not observed in body position alteration to avoid contact with the interior vehicle cabin, in over-the-shoulder checks, and in forward leans during yield and merge maneuvers. Regarding trajectory analysis, sling use did not produce a significant difference in the error metrics between the actual and ideal paths. In completion of turning maneuvers, however, operative extremity was significant for left turns, with greater error against the ideal path observed from those in the left operative cohort compared to those in the right operative cohort. For the right turn, however, operative extremity was not found to be a significant factor. In addition, the GPS data accuracy proved insufficient to support comparison against the ideal path. Overall, findings from this study provide metrics beyond those used in Badger, et al. that can be used in answering when it is safe for rotator cuff repair patients to return-to-drive. With the limited differences observed as a function of study timepoint and sling use, it is recommended that patients are able to safely return-to-drive at two weeks post-operation. If anything, results suggest that overcompensation, as inferred from observation of safer driving behaviors than normal, is present during some experimental timepoints, particularly post-operative week 2. / Master of Science / Current recommendations based on when it is safe for rotator cuff repair patients to return-to-drive are not standard because of a lack of suitable evidence. Previous work and recommendations rely on surveys and simulators which do not create fully realistic conditions and are subject to biases. To address the limitations of previous work, Badger et. al (2022) studied actual rotator cuff repair patients on public roads prior and following operation at multiple timepoints. Badger et al., however, did not consider the potential adaptations in driver behavior due to mobility restrictions and the perception of inferiority due to injury. Additionally, the degree of success of the adaptive driving behaviors based on the error between the actual vehicle path taken and a defined ideal path have not been explored in conjunction with the injury. This investigation is based on the pre-existing data collected by the Virginia Tech Transportation Institute (VTTI) and Carilion Clinic as used in the analysis performed by Badger et al. (2022). RCR patients (n=27) executed identical driving maneuvers and drove the same route before operation and at 2-, 4-, 6-, and 12-weeks post operation. Behavioral observations were recorded and related to relevant vehicle sensor readings. To construct vehicle paths, data was taken from the on-board data acquisition system (DAS). Participants adopted different behaviors, such as using the right hand to use the turn signal when the left arm was in a sling and the left hand to operate the gear shifter when the right arm was on a sling, to assist in combating mobility restrictions. One-handed steering was also more prominent during periods of sling-use. Sling-use, however, did not produce a significant difference in error between the actual vehicle path taken and the ideal path available to the driver. For left-operated participants completing left turns, there was also greater error in comparison to the ideal path than for the group of right-operated patients. However, there was not a difference between left- and right-operated arm participant error in completion of a right turn. The GPS data did not provide a suitable approximation of vehicle trajectory. Overall, findings from this study help to answer when it is safe for rotator cuff repair patients to return-to-drive through evaluation of the effectiveness of compensatory behaviors adopted by participants. With no significant difference in turn execution based on sling use, results suggest that patients can safely return-to-drive at two weeks post-operation. In fact, results suggest that overcompensation towards safer behaviors is present during some experimental timepoints, particularly post-operative week 2.

Increasingly, many of today’s vehicles offer Society of Automotive Engineers (SAE) partially automated driving (PAD) and a limited number of SAE conditionally automated vehicles (CAD) are being developed. Vehicles with PAD systems support the driver through longitudinal and lateral control inputs. However, during PAD the driver must be prepared to take control of the vehicle at any time, requiring them to monitor the environment and PAD system. In contrast, during CAD the driver is not required to monitor the environment or system but must take control when prompted by the system. Given the ability of CAD vehicles to operate in PAD and manual driving, it is important to consider drivers’ mode awareness, that is, their ability to follow the state of the automated system and predict the implications of this status for vehicle control and monitoring responsibilities. In addition, since CAD does not require drivers to keep their visual or attentional resources on the driving task or environment, drivers are allowed to perform secondary tasks (i.e., non-driving related tasks (NDRTs)). Given that drivers will freely choose what types of tasks they do during CAD it is important to build an understanding of whether drivers will choose to engage in NDRTs in the CAD state, and drivers’ ability to perform NDRTs during CAD. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in NDRTs, and their ability to perform scheduled smartphone NDRTs, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method to simulate a vehicle capable of Assisted Driving (similar to PAD) and Automated Driving (similar to CAD). A total of 36 drivers completed the on-road experiment, and experienced stable periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ mode awareness was measured. Drivers’ performance of NDRTs and behavioral adaptation during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in unscripted NDRTs during automated driving, participants were allowed to freely choose to engage in smartphone NDRTs between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results from a mixed-model ANOVA showed that participants’ mode awareness of their role in Automated Driving statistically significantly increased from the first activation to the subsequent activations. There was no statistically significant effect of activation period on drivers’ willingness to engage in NDRTs, as measured by the mean percentage of time drivers chose to engage in NDRTs during Automated Driving, or driver’s ability to perform tasks, as measured by the mean task completion time of the experimenter administered smartphone NDRTs. The mean number of participants who chose to engage in an NDRT (73.8%) and the percentage of time spent on NDRTs per Automated Driving activation period (M=20.37%; SD=23.9), indicated that drivers were willing to engage in NDRTs during Automated Driving. In addition, drivers showed a high level of task performance, completing 95% of the scheduled NDRTs correctly. Altogether, these results suggest that drivers are willing to engage in and perform NDRTs during Automated Driving and that driver behavior during Automated Driving is consistent and stable during a two-hour exposure period. Finally, the findings indicate that requiring the participant to control the vehicle manually for a brief period prior to transitioning to a level of automation that allows the driver to take their visual and attentional resources away from the roadway environment results in statistically significantly less NDRT engagement compared to when participants transition directly to this level of automation. Overall, the findings from this study have methodological and potential system design implications that can help guide the future research on and design of automated driving systems. / M.S. / Increasingly, many of today’s vehicles offer automated driving technology (i.e., Assisted Driving) that support the driver through steering, braking, and accelerating the vehicle. However, during this level of automation the driver must be prepared to take control of the vehicle, requiring them to monitor the environment and the automated driving system. In addition, a limited number of vehicles offer automated driving technology (i.e., Automated Driving) that controls the vehicle and does not require the driver to monitor the environment or system, however, the driver must take control when prompted by the system. Vehicles capable of Automated Driving can also operate in Assisted and manual driving modes. Given the ability of Automated Driving vehicles to operate in Assisted and manual driving, it is important to consider driver’s ability to follow and predict the behavior of the automated system. In addition, since Automated Driving does not require drivers to keep their eyes or mind on driving or monitoring the road, drivers are allowed to perform secondary tasks. Since drivers are free to choose what types of tasks they do during Automated Driving, it is important to understand whether drivers will choose to engage in Secondary tasks, and their ability to perform these tasks during Automated Driving. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in tasks, and their ability to perform scheduled smartphone tasks, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method. The WoZ method uses a concealed human to simulate an automated computer system, in this case an automated driving system. A total of 36 drivers completed the on-road experiment. The participants experienced periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ knowledge of who/what was controlling the vehicle and the driver’s role in the current automated mode was measured. Drivers’ performance of tasks during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in tasks during automated driving, participants were allowed to freely choose to engage in smartphone tasks between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results showed that participants’ ability to identify their role in Automated Driving increased from the first time they experienced the system to the subsequent times. There was no change in drivers’ willingness to engage in tasks or drivers’ ability to perform tasks as they gained more experience with the Automated Driving system. However, the level of task engagement indicated that drivers were immediately willing to engage in tasks during Automated Driving. Drivers also showed a high-level of task performance. Taken together, these findings indicate that drivers are willing to engage in and perform non-driving related tasks during Automated Driving. These findings can help guide future research focused on automated systems and the design of automated driving systems.

This study was undertaken to simulator test and evaluate a complete drowsy driver detection system. The goal of the study was to recommend optimal specifications for a system to be further studied in an actual vehicle. The system used a set of algorithms developed from previously collected data and a set of previously optimized advisory tones, advisory messages, alarm stimuli, and drowsiness countermeasures. Detection occurred if eye closure or lane excursion exceeded predetermined thresholds. Data were obtained from six sleep-deprived subjects who drove a motion base automobile simulator late at night. Each subject was trained in carefully observing lane boundaries, using a device which sounded an alarm if lane boundaries were exceeded. The performance aspect of the system dominated the detection process. None of the algorithms tracked well with the measures they were designed to estimate; correlations were much lower than expected. The algorithms relied heavily on the positioning of the vehicle relative to the lane. / Master of Science

In competitive golf, the player’s ability to hit the ball a long distance affects the score in a positive way. Swing kinematics is considered an important factor in driver performance; one way of improving swing kinematics is through strength and power training. Subsequently, high-level golfers and their coaches often explore novel ways of increasing the distance of a shot, in particular driver carry distance (CD). This licentiate thesis presents two studies with the overall aim of studying how swing kinematic and physical characteristics are associated with driver performance among intercollegiate golf players. The studies report swing kinematics associated with driver performance (study 1) and the impact of isokinetic rotational training on driver performance and swing kinematic variables (study 2). The methods used were (1) a cross-sectional correlation study (n=15) and (2) an open trial intervention study (n=20). The studies investigated (1) the relationship between golf swing kinematics and driver performance, and (2) the impact of strength training on swing kinematics and driver performance variable. The results show variables that were distinctive for the club head speed (CHS) were mainly during the downswing: greater X-factor stretch; and higher pelvis peak speed. Whilst, factors distinctive to the regression model for CD were mainly at impact: reduced thorax rotation; and greater thorax lateral bend. Nine weeks of isokinetic training increased seated rotational force and power, peak arm speed and arm acceleration, ball speed, and CD more compared to isotonic training. Even though isokinetic training did not increase CHS, it did result in greater CD. This licentiate thesis contributes to the understanding of which variables influence driver performance, in particular CD, among high-level golfers. Segmental interactions (pelvis-thorax), lead arm speed and acceleration, isokinetic and isotonic training. These results may guide training interventions aiming to improve driver and golf performance among high-level golfers, particularly those with a background of strength training. Future studies could investigate how the interaction between swing kinematics, clubhead trajectory, and driver performance variables differ between male and female golfers.

Fuel consumption is a major economical component of vehicles, particularly for heavy-duty vehicles. It is dependent on many factors, such as driver and environment, and control over some factors is present, e.g. route, and we can try to optimize others, e.g. driver. The driver is responsible for around 30% of the operational cost for the fleet operator and is therefore important to have efficient drivers as they also inuence fuel consumption which is another major cost, amounting to around 40% of vehicle operation. The difference between good and bad drivers can be substantial, depending on the environment, experience and other factors. In this thesis, two methods are proposed that aim at quantifying and qualifying driver performance of heavy duty vehicles with respect to fuel consumption. The first method, Fuel under Predefined Conditions (FPC), makes use of domain knowledge in order to incorporate effect of factors which are not measured. Due to the complexity of the vehicles, many factors cannot be quantified precisely or even measured, e.g. wind speed and direction, tire pressure. For FPC to be feasible, several assumptions need to be made regarding unmeasured variables. The effect of said unmeasured variables has to be quantified, which is done by defining specific conditions that enable their estimation. Having calculated the effect of unmeasured variables, the contribution of measured variables can be estimated. All the steps are required to be able to calculate the influence of the driver. The second method, Accelerator Pedal Position - Engine Speed (APPES) seeks to qualify driver performance irrespective of the external factors by analyzing driver intention. APPES is a 2D histogram build from the two mentioned signals. Driver performance is expressed, in this case, using features calculated from APPES. The focus of first method is to quantify fuel consumption, giving us the possibility to estimate driver performance. The second method is more skewed towards qualitative analysis allowing a better understanding of driver decisions and how they affect fuel consumption. Both methods have the ability to give transferable knowledge that can be used to improve driver's performance or automatic driving systems. Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.