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Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

Threat assessment (TA) method is vital in the decision-making process of intelligent vehicles (IVs), especially for ADAS systems. In the research of TA, the probabilistic threat assessment (PTA) method is acting an increasing role, which can reduce the uncertainties of driver’s maneuvers. However, the driver behavior model (DBM) used in present PTA methods was mainly constructed by limited data or simple functions, which is not entirely reasonable and may affect the performance of the TA process. This work aims to utilize crash data extracted from Event Data Recorder (EDR) to establish more accurate DBM and improve the current PTA method in rear-end situations. EDR data with responsive maneuvers were firstly collected, which were then employed to construct the initial DBM (I-DBM) model by using the multivariate Gaussian distribution (MGD) framework. Besides, the model was further subdivided into six parts by two important risk indicators, Time-to-collision (TTC) and velocity.

Automotive safety is always the focus of consumers, the selling point of products, the focus of technology. In order to achieve automatic driving, interconnection with the outside world, human-automatic system interaction, the security connotation of intelligent and connected vehicles (ICV) changes: information security is the basis of its security. Functional safety ensures that the system is operating properly. Behavioral safety guarantees a secure interaction between people and vehicles. Passive security should not be weakened, but should be strengthened based on new constraints. In terms of information safety, the threshold for attacking cloud, pipe, and vehicle information should be raised to ensure that ICV system does not fail due to malicious attacks. The cloud is divided into three cloud platforms according to functions: ICVs private cloud, TSP cloud, public cloud.

An energy management strategy is needed to optimally allocate the driver's power demands to different power sources in the fuel cell hybrid vehicles. The driver's power demand is modelled as a Markov process in which the transition probabilities are estimated on the basis of the observed sample paths. The Markov Decision Process (MDP) theory is applied to design a stochastic energy management strategy for fuel cell hybrid vehicles. This obtained control strategy was then tested on a real time simulation platform of the fuel cell hybrid vehicles. In comparison to the other 3 strategies, the constant bus voltage strategy, the static optimization strategy and the dynamic programming strategy, simulations in the Beijing bus driving cycle demonstrate that the obtained stochastic energy management strategy can achieve better performance in fuel economy in the same demand of dynamic.

Studying traffic accidents by using naturalistic driving data has become increasingly appealing for its potential benefits in improving road safety. This paper presents findings from a field test which has been conducted on 50 taxis in the urban areas of Beijing for 10 months using Video Drive Recorders (VDRs). The VDR used in this study could record the information of vehicle front view video, vehicle states, as well as driver operations immediately before and after an event. The drivers were given no specific instructions during the test, and the instrumentation for data collection was unobtrusive. Important safety-relevant parameters, such as vehicle speed, pre-event maneuver, time headway, time-to-collision, and driver reaction time, were calculated with precision. Based on these parameters, an analysis into features and causes of rear-end conflicts is performed.

Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.

Vehicle automation is a fundamental approach to reduce traffic accidents and driver workload. However, there is a notable risk of pushing human drivers out of the control loop before automation technology fully matures. Cooperative driving (or vehicle co-piloting) is a novel paradigm which is defined as the vehicle being jointly navigated by a human driver and an automatic controller through shared control technology. Indirect shared control is an emerging shared control method, which is able to realize cooperative driving through input complementation instead of haptic guidance. In this paper we first establish an indirect shared control method, in which the driver’s commanded input and the controller’s desired input are balanced with a weighted summation. Thereafter, we propose a predictive model to capture driver adaptation and trust in indirect shared control.

Some rollover testing methodologies require specification of vehicle kinematic parameters including travel speed, vertical velocity, roll rate, and pitch angle, etc. at the initiation of vehicle to ground contact, which have been referred to as touchdown conditions. The complexity of the vehicle, as well as environmental and driving input characteristics make prediction of realistic touchdown conditions for rollover crashes, and moreover, identification of parameter sensitivities of these characteristics, is difficult and expensive without simulation tools. The goal of this study was to study the sensitivity of driver input on touchdown parameters and the risk of rollover in cases of steering-induced soil-tripped rollovers, which are the most prevalent type of rollover crashes. Knowing the range and variation of touchdown parameters and their sensitivities would help in picking realistic parameters for simulating controlled rollover tests.

Multiple laboratory dynamic test methods have been developed to evaluate vehicle crashworthiness in rollover crashes. However, dynamic test methods remove some of the characteristics of actual crashes in order to control testing variables. These simplifications to the test make it difficult to compare laboratory tests to crashes. One dynamic method for evaluating vehicle rollover crashworthiness is the Dynamic Rollover Test System (DRoTS), which simulates translational motion with a moving road surface and constrains the vehicle roll axis to a fixed plane within the laboratory. In this study, five DRoTS vehicle tests were performed and compared to a pair of unconstrained steering-induced rollover tests. The kinematic state of the unconstrained vehicles at the initiation of vehicle-to-ground contact was determined using instrumentation and touchdown parameters were matched in the DRoTS tests.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

Accurate identification of driver’s braking intention is essential in advanced driver assistance system and can make the driving process more comfortable and trustworthy. In this paper, a novel method for driver braking intention identification in cut-in scenarios was proposed by using driver’s gaze information and motion information of cut-in vehicles. Firstly, a "looking in and looking out" experimental platform including three eye-tracking cameras and one front-view camera was built to collect driver's gaze information and the vehicle motion information. Secondly, driver’s gaze features and motion features of cut-in vehicles were selected and the braking intention identification performance of several decision tree-based ensemble learning algorithms was compared. Thirdly, the feature importance was analyzed by using SHAP (SHapley Additive exPlanations) values. This novel method of braking intention identification makes full use of in-vehicle camera sensors.

An autonomous emergency braking (AEB) system can detect emergency conditions using sensors (e.g., radar and camera) to automatically activate the braking actuator without driver input. However, during the hard braking phase, crash conditions for the restraint system can easily change (e.g., vehicle velocity and occupant position), causing an out-of-position (OOP) phenomenon, especially for unbelted occupants entering the airbag deployment range, which may lead to more severe injuries than in a normal position. A critical step in reducing the injury of unbelted occupants would be to design an AEB system while considering the effect of deployed airbags on the occupants. Thus far, few studies have paid attention to the compatibility between AEB and airbag systems for unbelted occupants. This study aims to provide a method that combines AEB and airbag systems to explore the potential injury reduction capabilities for unbelted occupants.

Safety of buses is crucial because of the large proportion of the public transportation sector they constitute. To improve bus safety levels, especially to avoid driver error, which is a key factor in traffic accidents, we designed and implemented an intelligent bus called iBus. A robust system architecture is crucial to iBus. Thus, in this paper, a novel self-driving system architecture with improved robustness, such as to failure of hardware (including sensors and controllers), is proposed. Unlike other self-driving vehicles that operate either in manual driving mode or in self-driving mode, iBus offers a dual-control mode. More specifically, an online hot standby mechanism is incorporated to enhance the reliability of the control system, and a software monitor is implemented to ensure that all software modules function appropriately. The results of real-world road tests conducted to validate the feasibility of the overall system confirm that iBus is reliable and robust.

Steering control devices are used by people who have difficulty gripping the steering wheel. These devices have projections that may extend up to 14 cm toward the occupant. Testing indicated that contact with certain larger steering control devices with tall rigid projections could severely injure a driver in a frontal collision. In order to reduce this injury risk, an alternative, less injurious design was developed and tested. This design, which included replacing unyielding aluminum projections with compliant plastic ones, produced significantly lower peak contact pressure and less damage to the chest of a cadaver test subject, while maintaining the strength necessary to be useful.

Electric vehicles are capable of more flexible drivetrain configurations, such that driving dynamics of each wheel could be controlled independently to increase its stability and maneuverability bounds. We hereby propose a configuration consisting of four wheel independent driving and front and rear axle modular steering. The vehicle implements drive-by-wire technology, which means the control program running on vehicle control computer will have direct control authority of the vehicle under normal driving conditions, based on inputs of higher level systems such as human drivers and autonomous driving programs. Both the torque allocation on four wheels and the steering allocation on axles are completely independent on the mechanical hardware level, thus the vehicle is able to harness adverse contact conditions with confidence.

Low back pain has a higher prevalence among drivers who have long term history of vehicle operations. Vehicle vibration has been considered to contribute to the onset of low back pain. However, the fundamental mechanism that relates vibration to low back pain is still not clear. Little is known about the relationship between vibration exposure, the biomechanical response, and the physiological responses of the seated human. The aim of this study was to determine the vibration frequency that causes the increase of muscle activity that can lead to muscle fatigue and low back pain. This study investigated the effects of various vibration frequencies on the lumbar and thoracic paraspinal muscle responses among 11 seated volunteers exposed to sinusoidal whole body vibration varying from 4Hz to 30Hz at 0.4 g of acceleration. The accelerations of the seat and the pelvis were recorded during various frequency of vibrations. Muscle activity was measured using electromyography (EMG).

In order to reduce driver's anxiety about range and energy, a direct and effective approach is to offer the remaining driving range based on the vehicle's states. Consequently, the estimation accuracy of the battery's remaining energy is very important. This paper introduces a experiment-based model for predicting the remaining energy, which considers many factors, such as current, temperature, difference between battery cells, and so on. This approach ensures the accuracy of the remaining driving range. Finally the method is validated through the environment space test. Validation results show that this method can offer exact remaining energy, which reduces the estimation error of the remaining range greatly.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

Smart vehicles have become an important development direction of the transformation and upgrading of the automotive industry. Highly intelligent smart vehicles can free human drivers from driving tasks, endowing cars with the mobility and instrument properties. Smart cockpits integrate the media for interactions between humans and environments inside and outside cars. This paper has explored the components of smart cockpits, sorted out three development stages of smart cockpits from such three dimensions as man, car and environment, analyzed the characteristics of the second development stage (Stage 2.0), and illustrated the necessity of the competition between smartphones and head units at the second stage. Based on the comparison of merits and demerits between smartphones and head units, this paper has proposed three principles for an ideal division of duties of smartphones and head units.