Recognizing traffic signs is an essential component of intelligent driving systems' environment perception technology. In real-world applications, traffic sign recognition is easily influenced by variables such as light intensity, extreme weather, and distance, which increase the safety risks associated with intelligent vehicles. A Chinese traffic sign detection algorithm based on YOLOv4-tiny is proposed to overcome these challenges. An improved lightweight BECA attention mechanism module was added to the backbone feature extraction network, and an improved dense SPP network was added to the enhanced feature extraction network. A yolo detection layer was added to the detection layer, and k-means++ clustering was used to obtain prior boxes that were better suited for traffic sign detection. The improved algorithm, TSR-YOLO, was tested and assessed with the CCTSDB2021 dataset and showed a detection accuracy of 96.62%, a recall rate of 79.73%, an F-1 Score of 87.37%, and a mAP value of 92.77%, which outperformed the original YOLOv4-tiny network, and its FPS value remained around 81 f/s. Therefore, the proposed method can improve the accuracy of recognizing traffic signs in complex scenarios and can meet the real-time requirements of intelligent vehicles for traffic sign recognition tasks.
Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle's travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle's characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions.
Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use of a random forest classifier was explored to analyze the relative importance of hydrometeorological indices in developing the leptospirosis model and to evaluate the performance of models based on the type of indices used, using case data from three districts in Kelantan, Malaysia, that experience annual monsoonal rainfall and flooding. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity, and temperature were transformed into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, weekly case occurrences were classified into binary classes "high" and "low" based on an average threshold. Seventeen models based on "average," "extreme," and "mixed" indices were trained by optimizing the feature subsets based on the model computed mean decrease Gini (MDG) scores. The variable importance was assessed through cross-correlation analysis and the MDG score. The average and extreme models showed similar prediction accuracy ranges (61.5-76.1% and 72.3-77.0%) while the mixed models showed an improvement (71.7-82.6% prediction accuracy). An extreme model was the most sensitive while an average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The rainfall variable (extreme) was the most important in classifying the leptospirosis occurrence while streamflow was the least important despite showing higher correlations with leptospirosis.
As physical abilities and health decline with age, older adults tend to lose their driving abilities, which affects their mobility. As mobility is important to older adults' wellbeing, there is a need to explore alternative modes of transportation to increase their ability to actively participate in society. Hence, this paper aims to understand the characteristics of private chauffeuring and companionship services for older adults, and to assess their possible effects on older adults' wellbeing. We gathered the views of transport operators, government agencies, and city councils that offer private chauffeuring and companionship services for older adults. We frame the model of private chauffeuring and companionship services as alternative mobility for older adults and outline a conceptual framework for its possible effects on their wellbeing. The underlying mobility characteristics were availability, accessibility, safety, and affordability-all of which influence wellbeing. The study found that the private chauffeuring and companionship model for older adults includes an additional model of government-to-consumer services in addition to the existing peer-to-peer and business-to-consumer services. While the services are available, the services provided are not standardized, with different operators offering different services and prices, and limiting certain geographical areas. Transport operators perceived that the services they offer promote older adults' physical and mental health, improve their social participation in the community, and empower them in making their travel decisions. The findings of the paper provide insights for policy makers for future planning of alternative transportation for older adults to enhance their mobility.
Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone's built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add "noise" to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver's driving behavior.
Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin's minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.
Introduction: To determine the effectiveness of the enhance enforcement programmes (The Ops) on the percentage of seatbelt wearing among front occupants in Malaysia.
Methodology: The roadside observations for measuring the seatbelt wearing among front occupant were conducted before, two weeks and six months after the Ops. The study was conducted in selected states representing four different zones (Northern, Southern, Eastern and Central zones) of Peninsular Malaysia.
Result: A total of 12,298 drivers and 11,212 front occupants were observed for their seatbelt wearing status through out the study. Percentage of seatbelt wearing among drivers and front passengers were increased from the baseline of 82.6 % and 74.4 % to 92 % (95 % CI: 91.2, 92.7) and 87.0 % (95 % CI: 85.9, 88.0) after two week, but declined to 85.7 % (95 % CI: 84.4, 86.8) and 76.8 % (95 % CI: 75.2, 78.3) after six months of the Ops respectively. Pre and post analysis revealed that after 2 weeks, the Ops were significantly effective in increasing the seatbelt wearing among front passengers and drivers with the RR (95 % CI) of 1.17 (1.14, 1.20) and 1.12 (1.10,1.13) respectively. However, after six months, the effectiveness of the Ops was reduced for both type of vehicle occupant.
Conclusion: This study sheds light on the importance of the enhance enforcement programme for increasing the seatbelt wearing in Malaysia. However, in order to give more impact on seatbelt wearing, the strategy and the frequency of the enhance enforcement programme in Malaysia may need to be revised.
In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intrusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.
Novice drivers are at a greatly inflated risk of crashing. This led in the 20th century to numerous attempts to develop training programs that could reduce their crash risk. Yet, none proved effective. Novice drivers were largely considered careless, not clueless. This article is a case study in the United States of how a better understanding of the causes of novice driver crashes led to training countermeasures targeting teen driving behaviors with known associations with crashes. These effects on behaviors were large enough and long-lasting enough to convince insurance companies to develop training programs that they offered around the country to teen drivers. The success of the training programs at reducing the frequency of behaviors linked to crashes also led to several large-scale evaluations of the effect of the training programs on actual crashes. A reduction in crashes was observed. The cumulative effect has now led to state driver licensing agencies considering as a matter of policy both to include items testing the behaviors linked to crashes on licensing exams and to require training on safety critical behaviors. The effort has been ongoing for over a quarter century and is continuing. The case study highlights the critical elements that made it possible to move from a paradigm shift in the understanding of crash causes to the development and evaluation of crash countermeasures, to the implementation of those crash countermeasures, and to subsequent policy changes at the state and federal level. Key among these elements is the development of simple, scalable solutions.
The spread of the novel coronavirus COVID-19 resulted in unprecedented worldwide countermeasures such as lockdowns and suspensions of all retail, recreational, and religious activities for the majority of 2020. Nonetheless, no adequate scientific data have been provided thus far about the impact of COVID-19 on driving behavior and road safety, especially in Malaysia. This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. This was accomplished by comparing the driving behaviors of the same drivers in three periods: before COVID-19 lockdown, during COVID-19 lockdown, and after COVID-19 lockdown. Thirty people were previously recruited in 2019 to drive an instrumental vehicle on a 25 km route while recording their driving data such as speed, acceleration, deceleration, distance to vehicle ahead, and steering. The data acquisition system incorporated various sensors such as an OBDII reader, a lidar, two ultrasonic sensors, an IMU, and a GPS. The same individuals were contacted again in 2020 to drive the same vehicle on the same route in order to capture their driving behavior during the COVID-19 lockdown. Participants were approached once again in 2022 to repeat the procedure in order to capture their driving behavior after the COVID-19 lockdown. Such valuable and trustworthy data enable the assessment of changes in driving behavior throughout the three time periods. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers in particular being most affected by the traffic restrictions, driving significantly faster and performing more aggressive steering behaviors during the COVID-19 lockdown than any other time. Furthermore, the locations where the most speeding offenses were committed are highlighted in order to provide lawmakers with guidance on how to improve traffic safety in those areas, in addition to various recommendations on how to manage traffic during future lockdowns.
PURPOSE: A survey on new Malaysian drivers was conducted in Malaysia between year 2006-2009. The objective of this study was to look at the effectiveness of the present computerized visual screening tool and to compare it with the conventional testing method.
METHODS: A total of 3717 drivers aged 19±6 years, who had passed in the computerized visual screening, participated in this study.
RESULTS: 250 subjects achieved less than 0.3 LogMAR with their best eye and 83 subjects failed the Ishihara Test after retested using the conventional tool.
CONCLUSION: These finding showed the computerized visual screening test failed to filter some subjects according to the standards set.
KEYWORDS: Visual acuity; colour vision; driving; vision
A survey was carried out using a medical examination format that was prepared by the Malaysian Medical Association. The findings of the survey show that of the 266 cases surveyed, 64 drivers (24% of cases surveyed) are either totally unfit to drive or temporarily unfit to drive heavy goods and passenger vehicles. This is clear indication that the current format that is being used by the Road Transport Department is inadequate and needs to be reviewed. It must also be stressed that all the above 64 drivers have been certified fit using the existing Road Transport Department format and are currently driving in our highways and roads. Heavy vehicle goods and passenger vehicle drivers if not properly examined and medically certified are not only be endangering their own lives but also that of others. It is therefore recommended that based on the data available from this survey, the Road Transport Department should seriously consider adopting the medical examination format that was formalised by the Malaysian Medical Association and used in this survey.
We investigated the effect of participant-selected (PSel) and researcher-selected (RSel) music on urban driving behaviour in young men (N = 27; Mage = 20.6 years, SD = 1.9 years). A counterbalanced, within-subjects design was used with four simulated driving conditions: PSel fast-tempo music, PSel slow-tempo music, RSel music and an urban traffic-noise control. The between-subjects variable of personality (introverts vs. extroverts) was explored. The presence of PSel slow-tempo music and RSel music optimised affective valence and arousal for urban driving. NASA Task Load Index scores indicated that the urban traffic-noise control increased mental demand compared to PSel slow-tempo music. In the PSel slow-tempo condition, less use was made of the brake pedal. When compared to extroverts, introverts recorded lower mean speed and attracted lower risk ratings under PSel slow-tempo music. The utility of PSel slow-tempo and RSel music was demonstrated in terms of optimising affective state for simulated urban driving.
This review article aimed to analyse existing literature regarding the roles and performance of professional driving instructors (PDIs) in novice driver education (DE). A systematic classification scheme was adopted to analyse identified articles to determine the study context of PDIs in novice DE, the competency level of PDIs in relation to experienced and learner drivers and the contributions of PDIs to the novice driver learning process. A total of 14 original research articles were identified, with no systematic reviews or meta-analyses available. Overall, all of the articles were found to be inadequate in providing an in-depth understanding of the roles and performance of PDIs in novice DE. There is an urgent need to improve current understanding of the roles of PDIs in novice DE and to work towards an internationally recognised PDI management approach.
Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X-coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.
Citation: Consensus Guidelines on the Management of Epilepsy 2017. Epilepsy Council, Malaysia Society of Neuroscience.
Older version: Citation: Consensus Guidelines on the Management of Epilepsy 2010. Epilepsy Council, Malaysia Society of Neuroscience.
http://www.neuro.org.my/MSN_GUIDELINE/MSN_GUIDELINE_Consensus%20Guidelines%20on%20the%20Management%20of%20Epilepsy%202010.pdf
Keywords: CPG