Periodic Reporting for period 1 - RHAPSODY (Recognition of HumAn PatternS of Optimal Driving for safetY of conventional and autonomous vehicles)
Okres sprawozdawczy: 2021-06-01 do 2023-05-31
Road traffic injuries are a major public health problem in the WHO European Region and cause the premature death of over 80,000 people every year. They are the leading cause of death in young children and adults aged 5 to 29 years. In addition, about 2.7 million people are estimated to be seriously injured annually. These cause a substantial economic loss to society: up to 3% of the gross domestic product of any given country. Human factors are persistently the main cause of road crashes, with a percentage of 65%–95%. Therefore, it is crucial to understand them in-depth and suggest new approaches to shape safe driving behaviours.
An important gap in existing research is the absence of a robust methodology for the identification of: a) macroscopic driver profiles, b) repetitive microscopic driving patterns in terms of speed or heart beats e.g. before harsh events or manoeuvers and c) optimal driving and its characteristics.
In this context, the objective of RHAPSODY was to provide evidence for a shift of focus in driving behaviour models, from the unsafe to the optimal driving, through the analysis of the driving behaviour on both a macroscopic and microscopic level. The project shed light on the number of different driver profiles and driving patterns that exist and revealed how these patterns differentiate on an individual and driver population level. This research also provided insights on the relationship of these driver profiles and driving patterns with driver characteristics such as age, income and crash history data. Finally, RHAPSODY recognized the individual and driver population benchmarks of optimal driving, investigated the different optimal driving patterns that exist and explored what should be the individual recommendations to drivers to reach this optimal level.
An important gap in existing research is the absence of a robust methodology for the identification of: a) macroscopic driver profiles, b) repetitive microscopic driving patterns in terms of speed or heart beats e.g. before harsh events or manoeuvers and c) optimal driving and its characteristics.
In this context, the objective of RHAPSODY was to provide evidence for a shift of focus in driving behaviour models, from the unsafe to the optimal driving, through the analysis of the driving behaviour on both a macroscopic and microscopic level. The project shed light on the number of different driver profiles and driving patterns that exist and revealed how these patterns differentiate on an individual and driver population level. This research also provided insights on the relationship of these driver profiles and driving patterns with driver characteristics such as age, income and crash history data. Finally, RHAPSODY recognized the individual and driver population benchmarks of optimal driving, investigated the different optimal driving patterns that exist and explored what should be the individual recommendations to drivers to reach this optimal level.
During the conduction of this project, the following tasks were performed:
• Reviewed the current state-of-the-art about road safety II concept and authored a Journal paper on this topic
• Reviewed the current state-of-the-art about driving patterns and driver profiles and authored a Journal paper on this topic
• Reviewed the current state-of-the-art about AI applications on Transportation safety and authored a Journal paper and a Conference paper on this topic
• Developed and applied a methodological framework for the identification of driver profiles and authored a Conference paper and a Journal paper (under review) on this topic
• Developed and applied a methodological framework for the recognition of driving patterns and authored a Conference paper and a Journal paper (under review) on this topic
• Developed and applied a methodological framework for the recognition of optimal driving benchmarks and authored a Journal paper (under review) on this topic
• Developed and applied a methodological framework for the recognition of travel patterns and authored a Conference paper on this topic
• Developed and applied a methodological framework to link smooth and safe driving and authored a Conference paper on this topic
• Participation in 5 Conferences and 14 workshops to discuss and disseminate results
• Collaboration with 3 TUD Departments
• Attended 15 training courses
• Developed a website for the Rhapsody project
• Created a YouTube video to describe the project’s methodology to broader audiences
As for the driver profiles, the different driver profiles that exist in the examined sample were identified and are those of the i) less risky drivers, ii) the modest drivers and iii) the more aggressive drivers, which is a finding confirmed by previous literature. It was also found that less risky drivers can be clearly defined by both clustering algorithms especially compared to more aggressive drivers who present a more diverse behavior in terms of driving characteristics. Both algorithms used produced more robust results when the number of clusters was reduced. Finally, no significant association between driver characteristics and clusters was found other than that between clusters and drivers’ age but a larger sample should be collected though to further investigate this.
As for the driving pattern recognition, this research investigated the microscopic characteristics of driving behaviour around driving pulses of 3 types of harsh events of high intensity, namely braking, acceleration and cornering. To this end, a time-series clustering approach was proposed for driving pattern recognition using naturalistic driving data of speed and heart rate for each driver. Results showed that there are 4 to 6 repetitive speed patterns recognized during HB, HA and HC events. The investigation of patterns during harsh braking revealed a steady speed before events, whereas before HA and HC events, a mild to intense deceleration is noticed in almost all patterns. The distribution of observations across patterns of harsh events was not uniform, with some patterns being significantly under-represented. As for the heart rate patterns, it was discovered that these also exist and present upward and downward trends before these events, depending of course on the type of the event.
Regarding the optimal driving benchmark recognition, the boundaries of risky and typical driving were found and those driving periods where quantified for each driver of the sample. For the majority of the drivers recognized as optimal, they appeared not to exceed the speed limit. The rest of the drivers either spent a very short time duration inside the risky period for time to collision (TTC) and crash index (CI) or a longer time duration inside the typical driving period or both. It was highlighted there is no specific optimality pattern that drivers follow but there are several different patterns instead.
• Reviewed the current state-of-the-art about road safety II concept and authored a Journal paper on this topic
• Reviewed the current state-of-the-art about driving patterns and driver profiles and authored a Journal paper on this topic
• Reviewed the current state-of-the-art about AI applications on Transportation safety and authored a Journal paper and a Conference paper on this topic
• Developed and applied a methodological framework for the identification of driver profiles and authored a Conference paper and a Journal paper (under review) on this topic
• Developed and applied a methodological framework for the recognition of driving patterns and authored a Conference paper and a Journal paper (under review) on this topic
• Developed and applied a methodological framework for the recognition of optimal driving benchmarks and authored a Journal paper (under review) on this topic
• Developed and applied a methodological framework for the recognition of travel patterns and authored a Conference paper on this topic
• Developed and applied a methodological framework to link smooth and safe driving and authored a Conference paper on this topic
• Participation in 5 Conferences and 14 workshops to discuss and disseminate results
• Collaboration with 3 TUD Departments
• Attended 15 training courses
• Developed a website for the Rhapsody project
• Created a YouTube video to describe the project’s methodology to broader audiences
As for the driver profiles, the different driver profiles that exist in the examined sample were identified and are those of the i) less risky drivers, ii) the modest drivers and iii) the more aggressive drivers, which is a finding confirmed by previous literature. It was also found that less risky drivers can be clearly defined by both clustering algorithms especially compared to more aggressive drivers who present a more diverse behavior in terms of driving characteristics. Both algorithms used produced more robust results when the number of clusters was reduced. Finally, no significant association between driver characteristics and clusters was found other than that between clusters and drivers’ age but a larger sample should be collected though to further investigate this.
As for the driving pattern recognition, this research investigated the microscopic characteristics of driving behaviour around driving pulses of 3 types of harsh events of high intensity, namely braking, acceleration and cornering. To this end, a time-series clustering approach was proposed for driving pattern recognition using naturalistic driving data of speed and heart rate for each driver. Results showed that there are 4 to 6 repetitive speed patterns recognized during HB, HA and HC events. The investigation of patterns during harsh braking revealed a steady speed before events, whereas before HA and HC events, a mild to intense deceleration is noticed in almost all patterns. The distribution of observations across patterns of harsh events was not uniform, with some patterns being significantly under-represented. As for the heart rate patterns, it was discovered that these also exist and present upward and downward trends before these events, depending of course on the type of the event.
Regarding the optimal driving benchmark recognition, the boundaries of risky and typical driving were found and those driving periods where quantified for each driver of the sample. For the majority of the drivers recognized as optimal, they appeared not to exceed the speed limit. The rest of the drivers either spent a very short time duration inside the risky period for time to collision (TTC) and crash index (CI) or a longer time duration inside the typical driving period or both. It was highlighted there is no specific optimality pattern that drivers follow but there are several different patterns instead.
During the literature review conducted for the RHAPSODY project, the following research gaps were identified and several methodological frameworks were developed to fill them:
• Identification of driver profiles
• Recognition of driving patterns
• Recognition of optimal driving benchmarks
• Detection of travel patterns
• Identify the relationship between smooth and safe driving
This project provided evidence for a shift of focus in driving behaviour models, from the unsafe to the optimal driving, through the analysis of the dynamic evolution of driving behaviour in time on both a macro and a microscopic level. Driving behaviour characteristics are part of the human factors that are persistently the main cause of road crashes, with a percentage of 65%–95%. Therefore, understanding them in-depth and suggesting new approaches to shape safe driving behaviours may potentially reduce the number of crashes that occur.
• Identification of driver profiles
• Recognition of driving patterns
• Recognition of optimal driving benchmarks
• Detection of travel patterns
• Identify the relationship between smooth and safe driving
This project provided evidence for a shift of focus in driving behaviour models, from the unsafe to the optimal driving, through the analysis of the dynamic evolution of driving behaviour in time on both a macro and a microscopic level. Driving behaviour characteristics are part of the human factors that are persistently the main cause of road crashes, with a percentage of 65%–95%. Therefore, understanding them in-depth and suggesting new approaches to shape safe driving behaviours may potentially reduce the number of crashes that occur.