The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process

In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.

Classification of Bus Driving Behaviour In Universiti Malaysia Sabah Campus

Driving behaviour has been studied by numerous researchers for the past few years. It includes the instantaneous driving behaviour observations and the drivers speed which are said to be influenced by many factors, such as the demographic measure of the drivers, environmental, passenger effect, and road characteristics. This paper describes the recent analysis and classification of driver behaviour in actual driving scenarios among the bus drivers in Universiti Malaysia Sabah (UMS) Main Campus, Kota Kinabalu. This research focussed on determining the riderships of bus in UMS campus, to investigate the differences of instantaneous driving behaviours of bus drivers during the acceleration phase when leaving bus stops, and to poduce the classification of the bus driving behaviour in UMS based on the driver’s accelerations. In order to achieve the objective of this study, observations were made for determining the riderships and the differences in instantaneous bus driving behaviour several times for each bus stops. For drivers speed and accelerations, a mobile applications called Speedometer GPS was used to obtain the data. Interview was conducted to a total number of 10 respondents to obtain their demographic measure. The results obtained shows the ridership of UMS bus is the highest in the afternoon peak. The instantaneous driving behaviour produce the head movement as the highest percentage during peak hour, and inattentive behaviour as the highest during the off peak hour. The bus drivers in UMS were classified as Aggressive and Calm Behaviour Category.

Acceleration and Deceleration Rates of Various Vehicle Categories at Signalized Intersections in Mixed Traffic Conditions

The acceleration and deceleration rates vary from one vehicle type to another. The same vehicle type also exhibits variations in acceleration and deceleration rates due to vast variation in their dynamic and physical characteristics, ratio between weight and power, driver behaviour during acceleration and deceleration manoeuvres. Accurate estimation of acceleration and deceleration rates is very important for proper signal design to ensure minimum control delay for vehicles, which are passing through the intersection. The present study measures acceleration and deceleration rates for four vehicle categories: Two-wheeler, Three-wheeler, Car, and Light Commercial Vehicle (LCV), by using Open Street Map (OSM) tracker mobile application. The acceleration and deceleration rates were measured at 24 signalized intersection approaches in Hyderabad and Warangal cities. The study also developed acceleration and deceleration models for each vehicle type and the developed models were validated based on field data. The results showed that the predicted acceleration and deceleration models showed close relation with those measured in the field. The developed models are useful in predicting average acceleration and deceleration rate for different vehicle types under mixed and poor lane disciplined traffic conditions.

Driver Behavior When Overtaking Cyclists Riding in Different Group Configurations on Two-Lane Rural Roads

The presence of cyclists on Spanish rural roads is ever increasing and currently frequent, and thus becoming a serious safety concern. In rural environments, the risk of a crash is higher than in rural areas. The main cause is the higher speed of motor vehicles during overtaking manoeuvres. This manoeuvre is especially challenging when cyclists ride in groups as they may change size, length, shape, and speed along their route. These variables and those related to road cross-section can influence driver behaviour when overtaking a group of cyclists. To study this, instrumented bicycles were used to ride along five road segments with different geometric and traffic characteristics. Cyclists rode individually and in groups. Overtaking was evaluated by analysing the lateral distance, the speed, and other characteristics of the manoeuvre. Wider roads presented higher lateral clearances and overtaking speeds. Narrower roads had a high opposing lane invasion but a high level of compliance with the minimum lateral clearance. A higher clearance and lower speed of overtaking vehicles was registered when cyclists rode in line. Compliance with the 1.5 m clearance depended on the group configuration, being higher when cyclists rode in line. However, overtaking cyclists riding two abreast presented more accelerative manoeuvres, especially on narrow roads.

Driver behaviour and traffic accident involvement among professional heavy semi-trailer truck drivers in China

The purpose of this study was to develop a driving behavior scale for professional drivers of heavy semi-trailer trucks in China, and study the causes of such driving behavior and its impact on traffic safety operation. Data was processed by IBM SPSS 25. In addition to principal component analysis, Promax rotation, Bartlett’s test, Cronbach’s alpha, correlation analysis and binary logistic regression were examined. A DBQ with 4 dimensions and 20 items, and a PDBQ with 1 dimension and 6 items were developed for professional drivers of heavy semi-trailer trucks in China. The KMO coefficients of PDBQ and DBQ were 0.822 and 0.852, respectively, and the significant level of Bartlett’s popularity test was p < 0.0001. The accident prediction model showed that the variables related to traffic accidents were negligence/lapses and driving time of heavy semi-trailer truck drivers. 1–5 a.m. was found to be the most dangerous period for drivers of medium and heavy semi-trailer trucks, during which accidents were most likely to happen. As negligence/lapses increased by one unit, the probability of traffic accidents increased by 2.293 times.

Designing in-car emotion-aware automation

Driver behaviour recognition is of paramount importance for in-car automation assistance. It is widely recognized that not only attentional states, but also emotional ones have an impact on the safety of the driving behaviour. This research work proposes an emotion-aware in-car architecture where it is possible to adapt driver’s emotions to the vehicle dynamics, investigating the correlations between negative emotional states and driving performances, and suggesting a system to regulate the driver’s engagement through a unique user experience (e.g. using music, LED lighting) in the car cabin. The relationship between altered emotional states induced through auditory stimuli and vehicle dynamics is investigated in a driving simulator. The results confirm the need for both types of information to improve the robustness of the driver state recognition function and open up the possibility that auditory stimuli can modify driving performance somehow.

Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model

This paper presents the performance analysis of a latest-generation hybrid vehicle (Toyota Yaris 2020) with a testing campaign in real road conditions and a comparison with the previous model (Toyota Yaris 2017). The study was conducted by applying the Real Drive Truth Test protocol, developed by the research group, validated and spread to other full hybrid vehicles: Toyota Prius IV (2016) and Toyota Yaris 2017 (2017). In the case of the 2020 tests, the co-presence on board—deemed unsafe in the usual ways given the ongoing pandemic—was achieved through precise and sophisticated remote control. An on-board diagnostic computer, video transmission and recording equipment guarantee the virtual co-presence of a technical control room and a driver. Thus, several engineers can follow and monitor each vehicle via a 4G modem (installed in each vehicle), analysing data, route and driver behaviour in real-time, and therefore even in the presence of a single occupant in the car under test. The utmost attention has also been paid to adopting anti-COVID behaviours and safety standards: limited personal interactions, reduced co-presence in shared rooms (especially in the control room), vehicle sanitising between different drivers, computers and technicians and video technicians working once at a time. The comparison between the two subsequent vehicle models shows a significant improvement in the performance of the new generation Yaris, both in terms of operation in ZEV (zero-emission vehicle) mode (+15.3%) and in terms of consumption (−35.1%) and overall efficiency of the hybrid powertrain (+8.2%).