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Accurate distance estimation is crucial for ensuring the safety and efficiency of intelligent and future autonomous mobility. In this paper, we propose a method for real-time estimation of visible lane distance based on image data only from a single camera and an existing road lane marking estimation system. By leveraging camera intrinsic and height above the road, we utilize lane parallelism and geometric properties of frames from a camera to compute the distance to the furthest visible lane point. Intelligent recalculations of horizon level are performed for every frame, accommodating variations encountered during uphill or downhill driving scenarios. Moreover, the proposed solution is designed to be theoretically adaptable to gauge the distance to any detected object on the camera frame, not only road lanes. We evaluate the effectiveness of our method on a dataset created for this purpose, assessing estimation performance through comparison with ground truth data obtained from high-precision GPS measurements. The results indicate comparable estimation performance with other works. Coupled with the capability for real-time implementation in vehicles, we underscore the potential of our approach for advanced driver assistance systems (ADAS) in various aspects, such as safe driving speeds, obstacle avoidance, or collision prevention. This research contributes to the ongoing efforts in developing robust and practical solutions for autonomous mobility applications.

This survey aims to provide an overview of various methods for generating data-driven test scenarios for assessing automated driving systems (ADSs). The survey updates the overall process of scenario generation and categorizes the current methods using a systematic literature review of 64 studies identified between 2017 and 01/2023. Overall, we demonstrate that the data-driven scenario generation process should be updated by another process step, scenario fusion, leading to seven process steps: 1) scope definition, 2) primary data source selection, 3) primary data collection, 4) scenario identification, 5) scenario fusion, 6) scenario generation, and 7) scenario evaluation. “Scenario fusion” aims to fuse scenarios identified in different data sources for a better coverage of the ADSs’ operational design domains (ODDs) and a more comprehensive scenario description. Moreover, we introduce an improved definition for the representativity of test scenario catalogs, which helps improve the collection of traffic data using sampling plans. Also, we show that real driving and police accident data are the most commonly used data input sources. Besides, we illustrate that the ODD is often not defined. Finally, we discuss that the standardization of test scenario generation is difficult because most methods do not address specific ADSs and test environments, and do not provide standardized interfaces. Overall, we recommend comparing existing approaches using the same input data and researching the mutual supplementation of the existing methods. Finally, pre-defined case studies, further standardized terminology, and standards for test execution and evaluation can help speed up the standardization process.

Automated driving systems should be able to avoid road traffic accidents and drive more safely than human drivers do. Test scenarios derived from real-world data such as police accident data can help assess the safety performance of automated driving systems. In many countries, the police collect data and information about nearly every accident, resulting in a representative sample. However, the collected accident data often do not contain the exact conflicts that cause an accident. Therefore, we estimated the globally known three-digit accident type for German police accident data, describing accident-causing conflicts. The data supplemented by the estimated three-digit accident type can then be used in subsequent test scenario generation. Accordingly, this study presents the first classification model for predicting 30 types of turning accidents. We tested a CatBoost model and a large-language model called BERT, using different feature sets and model designs. Overall, the CatBoost model performed best when using accident descriptions and non-text features such as collision type. Anomaly detection performed before model training revealed additional knowledge-driven miscoding in the police data collection. In conclusion, the model can predict common accident types, such as left-turns with an oncoming straight-ahead driver. In contrast, the model fails to predict rare accident types, such as left-turns (with a lit arrow sign) with oncoming traffic. Future studies should focus on optimizing the developed model, handling data imbalances, and building models using police accident data from other countries.

Scenario-based testing will help to validate automated driving systems (ADS) and establish safer road traffic. To date, most data-driven test scenario generation methods rely primarily on one data source such as police accident data (PD), naturalistic driving studies, or video-based traffic observations (VOs). However, none of these data sources perfectly satisfies all the layers of the six-layer model for the description of test scenarios. Moreover, not all available data sources cover the same location and period of time. Therefore, we fused information from 1,648 scenarios extracted from a German VO with information from 74 scenarios extracted from German PD into a comprehensive new PD* database. Finally, PD* consisted of 74 accident scenarios extended, for example, by variables containing the dynamic information of the VO scenarios. Thus, PD* contained more than 350 variables, whereas PD contained only 269 and VO only 122 variables. For fusion, we followed the Find-Unify-Synthesize-Evaluation (FUSE) for Representativity (FUSE4Rep) process model using statistical matching. Subsequently, we derived three logical scenarios from PD* to test an autonomous emergency braking system (AEB) in a stochastic traffic simulation incorporating driver-behavior models. The quality of the fusion itself was satisfactory, and we propose improving the VO data collection process and observation time to obtain even better results.

Lane Keeping Assist System (LKAS) enhances comfort and safety while driving. It plays a significant role in the Advanced Driver Assistance System (ADAS) and future Automated Driving (AD). The LKAS solution aims to help the driver keep the vehicle within the road lines, preventing unintentional lane departure. Despite LKAS being an important solution for comfortable driving, robust LKAS steering control is still lacking, requiring constant driver intervention or premature LKAS deactivation. LKAS require optimal control solutions with real-time constraints. This paper comprehensively analyzes Model Predictive Control (MPC) for real-time LKAS applications. Classical and parameterized MPC schemes with distinct Quadratic Programming (QP) solvers are combined to evaluate LKAS closed-loop control performance and real-time constraints. A sideslip and lateral speed bicycle modes were used to evaluate classical, trivial, and exponential MPC schemes. Experimental results highlight the three MPC and QP-appropriate solutions with satisfactory reference tracking without steering command and real-time constraints violation.

Future vehicles will be increasingly connected to enable new applications and improve safety, traffic efficiency and comfort, through the use of several wireless access technologies, ranging from vehicle-to-everything (V2X) connectivity to automotive radar sensing and Internet of Things (IoT) technologies for intra-car wireless sensor networks. These technologies span the radiofrequency (RF) range, from a few hundred MHz as in intra-car network of sensors to hundreds of GHz as in automotive radars used for in-vehicle occupant detection and advanced driver assistance systems. Vehicle occupants and road users in the vicinity of the connected vehicle are thus daily immersed in a multi-source and multi-band electromagnetic field (EMF) generated by such technologies. This paper is the first comprehensive and specific survey about EMF exposure generated by the whole ensemble of connectivity technologies in cars. For each technology we describe the main characteristics, relevant standards, the application domain, and the typical deployment in modern cars. We then extensively describe the EMF exposure scenarios resulting from such technologies by resuming and comparing the outcomes from past studies on the exposure in the car. Results from past studies suggested that in no case EMF exposure was above the safe limits for the general population. Finally, open challenges for a more realistic characterization of the EMF exposure scenario in the connected car are discussed.

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.

An intelligent driver assistance system is developed in this study, which is able to remind the drivers to turn on the head lights or wipers through situation recognition method when driving at night or on rainy days. Furthermore, the object detection results from multiple perspective views are integrated, and the surrounding object detection results are produced for collision avoidance. The system is able to alarm the drivers based on the lightweight deep learning model and the distance estimation method when surrounding vehicles are too close. Experimental results show that the proposed methods and the chosen lightweight model in our proposed system obtain reliable performance and sufficient computational efficiency under limited computing resource. In conclude, our proposed system obtains high probability to be adopted for the development of advanced driver assistance systems (ADAS). The proposed system can not only assist the driver in determining the vision ahead, but also provide an instant overview of the vehicle’s surrounding conditions to enhance driving safety.

On average 3,700 people lose their lives on roads every day due to car accidents as a result of drivers’ distraction. In this research, a proposed hybrid approach is presented. The approach is based on deep learning to detect the driver’s actions and eliminate the driver’s distraction as a packed solution. The detection is performed by analyzing the driver’s actions and his head pose. The elimination is made by using voice commands that are based on trigger words, speech to text, and text classification models to access the car’s functions such as the air-condition, radio, etc. The results of the driver’s actions classification showed 94.1% accuracy on the AUC benchmark database for driver distraction achieving the state-of-the-art accuracy on this benchmark. The results of the command to text classification is 95.19% while the results of the head pose estimation show a 6.21-degree MAE in face angles detection. With using our car commands dataset, the domain of the speech recognition output is more focused on car commands. The previously mentioned algorithms are beneficial to the safety of the driver. He can use his voice to operate the car accessories. His alert state is monitored and he is warned through an alarm if a distraction is detected. However, this research is not concerned with the detection of retinal abnormalities such as sleeping with eyes open. The results of real-time testing show 0.080 second response time for the driver’s behavior classification and command following with the use of graphical processing units.

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.

Collision imminent steering is an automotive active safety feature designed to perform an evasive lane change maneuver when a forward collision cannot be avoided by braking alone. Previous work developed a nonlinear one-level model predictive controller to perform such a maneuver in a straight highway environment and at high speeds. In this article, a new formulation is presented that extends this capability to curved roads. The formulation includes a drivable tube concept, within which the maneuver is allowed to push the vehicle to its limits of handling, but only if the controller deems it necessary. Numerical simulation results showcase aggressive lane change maneuvers on curved roads at high speeds, allowing for both inside and outside lane changes, as well as single and double lane change maneuvers in a shorter distance than braking.

Automatic evasive steering maneuvers can outperform human-initiated steering maneuvers in emergency situations. A steering interface that decouples the steering wheel from the tires may enhance the efficiency of automatic steering maneuvers by providing full authority to the automation system. Yet, an alternative interface in which the steering wheel remains coupled to the tires has the advantages of enabling the driver to intervene in the event of an automation failure and preventing the human factors issues associated with decoupling the driver. In this paper, we present a driving simulator study with 64 participants where we compared four steering interface design schemes in their ability to enable successful obstacle evasion in emergency scenarios. The steering wheel was either decoupled from the tires and the automation was given full authority, or the steering wheel was coupled to the tires and the automation was provided high, low or no authority. The automation was designed to avoid all obstacles, except for the last one when it failed unexpectedly. Results uncovered a design tradeoff: as the authority of an agent (driver or automation) increases, the protection against the agent’s faults (provided by the other agent) reduces. The results also show that decoupled driving reduced driver’s vigilance and mode awareness and deprived the drivers of the authority required to intervene during automation faults. Coupled driving alleviated these issues but caused driver discomfort when designed with high automation authority and resulted in a larger number of collisions during perfect automation operation when designed with low automation authority.

Pedestrians are the most vulnerable road users, with around 23% of world road traffic fatalities. To prevent such traffic collisions, the Pedestrian Collision Warning System (PCWS) alerts the driver before an imminent collision. In order to protect worldwide pedestrians, the PCWS should take into account different pedestrian crossing behaviors and different road structures, especially pedestrians with risky behavior on unstructured environments. Since oblique crossing is the usual crossing way of pedestrians with risky behaviors, recognizing the pedestrian walking direction is one of the key factors to consider. However, existing systems focus more on detecting pedestrians rather than recognizing their walking direction. This paper highlights the safety of different kinds of pedestrians by presenting a novel approach used to estimate pedestrian’s orientation from a single-frame. Next, the estimated orientation is integrated into the proposed PCWS system. Our method involves Capsule Network based technique trained on pedestrian images. For this purpose, a new pedestrian orientation dataset taken from real city-scenes named SafeRoad was created, using a single camera mounted on a moving vehicle. TUD Multiview Pedestrian and Daimler datasets are thereafter used as a benchmark to evaluate the proposed approach. Experimental results show that Capsule Network exceeds significantly the accuracy performance on the three datasets compared to Convolutional Neural Network algorithms.

Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of them is the adaptive cruise control (ACC) system. The ACC system is a continuation of research on cruise control (CC) system, which integrates spacing control with the existing velocity control on the CC system. The vehicles with an ACC system guarantee traffic safety while at the same time ensure a well-driving sense. Many studies have demonstrated numerous control techniques applied as ACC controllers to accomplish uncertainty and perturbation issues. Nevertheless, most of the existing papers assumed the model vehicle dynamics as a linear time-invariant (LTI) system while designing the ACC controller. This paper proposed an ACC controller using the gain scheduling technique to deal with the model vehicle dynamics as a linear parameter varying (LPV) system. The passenger vehicle’s mass varies during ACC operation depending on how many passengers or loads on the vehicle’s trunks. Later, the vehicle’s mass is estimated by recursive least square (RLS) with a forgetting factor. Then, the disk margin is utilized to provide the high-level robustness at each operating or “frozen” point. The robustness performance will be analyzed using the worst-case gain metric while the uncertainty is modeled by integral quadratic constraints (IQC). The LPV system behavior, such as the rate vehicle’s mass, is also considered in the analysis. The effectiveness algorithm is validated through joint simulation between Matlab/Simulink and PreScan. The last, the comparison performance between gain scheduling and fixed gain ACC controller is evaluated.

Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. In this paper, we propose a robust driver distraction detection system that extracts the driver’s state from the recordings of an onboard camera using Deep Learning. We consider ten driving activities, which consist of one normal driving and nine distracted driving behaviors. Nine drivers were included in the experiments, and each one was asked to perform the ten activities in naturalistic and simulated driving situations. The main feature of the proposed solution is the extraction of the driver’s body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our dataset and 95% on the public AUC dataset.

In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle's future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities.

Today, several studies have been concretized in the areas of robotics, self-driving cars, intelligent assistance systems, and so on. Developing an increasingly optimal neural network in terms of accuracy and processing speed for resource-limited systems has become a major research trend. Some research orientations include focusing on developing solutions to optimize machine learning models and learning parameters. In this study, we investigated an optimization solution for learning hyperparameters of adaptive learning systems for improving object recognition accuracy. The proposed method was developed from a framework searching a set of learning hyperparameters based on the evaluation of the previous CNN model with the collected dataset during the movement of advanced driver assistance systems (ADAS) equipment. The proposed solution consists of some major steps in a loop of adaptive learning system, such as (1) training an initial recognition model, (2) locating and receiving image data of different cases of the object during ADAS movement based on object tracking process, (3) finding optimal hyperparameters on the found dataset based on the previous recognition model, and (4) using the trained recognition model to update the current recognition model. The experimental results proved that the trained recognition model was capable of being more intelligent and displayed more diverse recognition than the previous model. The updated task for the recognition model was continuously repeated throughout the ADAS life. This approach supports and enables the recognition system to be self-adaptive and more intelligent in real life settings without manually processing.

Motion planning plays an essential role in designing self-driving functions for connected and autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to follow, fulfilling different requirements, such as safety, efficiency, and passenger comfort. In this area, algorithms must also meet strict real-time expectations, since, especially in an emergency, the decision time is limited, which raises a trade-off for the feasibility requirements. This article proposes a hierarchical path planning solution for evasive maneuvering, where a Twin Delayed DDPG reinforcement learning agent generates the parameters of a geometric path consisting of chlotoids and straight sections, and an underlying model predictive control loop fulfills the trajectory following tasks. The method is applied to the automotive double lane-change test, a common emergency situation, comparing its results with human drivers' performance using a dynamic simulation environment. Besides the test's standardized parameters, a broader range of topological layouts is chosen, both for the training and performance evaluation. The results show that the proposed method highly outperforms human drivers, especially in challenging situations, while meeting the computational requirements, as the pre-trained neural network and path generation algorithm can provide a solution in an instant, based on the experience gained during the training process.

The effective assessment of drivers' driving capability under the condition of an advanced driver assistance system is of great significance to the precise switching of driving rights between human and machine and the promotion of the development of man-computer collaboration. In this study, real-time collected warning data from bus driver state monitoring system (DSMS) and advanced driver assistance system (ADAS) were utilized to determine the drivers' comprehensive driving capability indicators. The information utility and interaction of the indicators were considered, and an integrated weight method based on standard deviations was proposed. This method was used to combine the entropy weight method and improved analytic network process (ANP), to evaluate the drivers' comprehensive driving capability under man-computer cooperative driving conditions in real time. The results show that the entropy weight method and improved ANP algorithm have good consistency and are significantly correlated and that the integrated weight method is effective and dependable. The top four indicators in the integrated weighting results were eye closure (0.241), yawn (0.210), rapid deceleration (0.186), and lane departure (0.159). Drivers' comprehensive driving capability scores were concentrated in the score range of 1 to 6, with the lowest scores in zones A and B for stages 2, 11 and 21. Therefore, it is necessary to further explore the relationship between driver behavior, vehicle status and road traffic environment within the score range of 1 to 6 so that the man-computer interaction can be optimized and the driver's comprehensive driving capability can be improved.

Recently, the ever-increasing vehicle population has become a severe challenge to traffic safety, especially the problem of a single-vehicle overtaking a platoon on the Two-Lane Two-Way (TLTW) road. Platooning has the potential to improve traffic efficiency and safety. However, there exists a perilous situation of “Neither overtake nor give up” when the single-vehicle overtakes a platoon on the TLTW road. This paper presents a flexible framework to automatically filter a large quantity of Advanced Driver Assistance Strategies (ADAS) and select the most suitable driver assistance information for the single-vehicle overtakes a platoon on the TLTW road. A step-by-step Single Vehicle Overtakes Platoon (SVOP) algorithm is designed to generate the coarse ADAS, which had given plenty of consideration to the vehicle safety, traffic efficiency, and driving comfort. Then, this paper obtains the raw data about the single-vehicle overtakes a platoon on the TLTW by using CARLA, which can help us to get 20 drivers’ upper and down boundaries of both velocity and acceleration. In addition, the extracted ranges of velocity and acceleration are used to quantitatively analyse the drivers’ driving features and filter the ADAS information. Finally, a Bayesian nonparametric approach is developed to segment driver’s driving raw data temporal sequences into small analytically interpretable components without using prior knowledge. So that the accurately overtaking characteristics can be obtained, and the ADAS can be further filtered. Experimental results demonstrate that the obtained coarse ADAS are only valid in theory but not acceptable by most of the drivers. Nonetheless, by leveraging the nonparametric Bayes algorithm, the driver’s overtaking behavior can be divided into different primitives, from which some could obtain the driver’s acceptance range for the velocity and acceleration. 92.3% ~ 94.78% invalid SVOP ADAS could be filtered out by leveraging the primitive-based SVOP approach. Thus, after filtering, the overtaking scheme is the most acceptable strategy for drivers.

The driver's physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human's health status. When detecting the driver's HR, using traditional contact-type devices might bring about the driver's distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver's HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver's HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers' HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver's HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the success-rate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).

This paper proposes a high-performance advanced driver assistance system that analyses driving scenes based on monocular cameras. The system identifies the ego-lane position and indicates if the vehicle is driving on an inner or outer lane. Dense optical flow analysis is performed and a fuzzy system is designed to achieve ego-lane position identification. An event warning application is implemented based on a hierarchical classifier and the results of ego-lane position identification. The proposed event warning system accurately issues events without having to detect vehicles first, making the system more responsive to potential approaching dangers. Also, the proposed system has a comprehensive ability to generate warnings on various types of events. The experimental results validate the effectiveness of the proposed schemes.

Drowsy driving is a prevalent and serious public health issue that deserves attention. Recent studies estimate that around 20% of car crashes have been caused by drowsy drivers. Nowadays, one of the main goals in the development of new advanced driver assistance systems is trustworthy drowsiness detection. In this paper, a drowsiness detection method based on changes in the respiratory signal is proposed. The respiratory signal, which has been obtained using an inductive plethysmography belt, has been processed in real time in order to classify the driver's state of alertness as drowsy or awake. The proposed algorithm is based on the analysis of the respiratory rate variability (RRV) in order to detect the fight against to fall asleep. Moreover, a method to provide a quality level of the respiratory signal is also proposed. Both methods have been combined to reduce false alarms due to the changes of measured RRV associated not with drowsiness but body movements. A driving simulator cabin has been used to perform the validation tests and external observers have rated the drivers' state of alertness in order to evaluate the algorithm performance. It has been achieved a specificity of 96.6%, a sensitivity of 90.3%, and Cohen's Kappa agreement score of 0.75 on average across all subjects through a leave-one-subject-out cross-validation. A novel algorithm for driver's state of alertness monitoring through the identification of the fight against to fall asleep has been validated. The proposed algorithm may be a valuable vehicle safety system to alert drowsiness while driving.

This paper proposes a low-cost cluster-based method called CLASES that employs a cluster members' data estimation mechanism to improve Quality of Services (QoS) of Vehicle-to-Everything communications for driving assistance with Crash Warning Application (CWA). The idea of the proposed method is to use the estimation mechanism by cluster heads and the estimation error correction mechanism by cluster members, instead of collecting member's data by intracluster communications, which most conventional cluster-based methods have used. Intracluster communications require some additional costs or reduce the bandwidth of intercluster communications; therefore, we can use the proposed method at low costs in comparison with the conventional methods. The proposed method also enables to control the number of active nodes by an advantage of clustering; that is, the proposed method appropriately adjusts the number of nodes that are likely to transmit frames simultaneously. Thus, data frames are transmitted as parallel as possible while suppressing the probability of frame collision errors, and the QoS improves. On the other hand, the disadvantage is that the error correction mechanism yields some additional frames, and the QoS deteriorates. We evaluated the performance of the proposed method in various parameters. The results show that the proposed method accommodate more nodes by 27 % than that of the method without clustering even at the realistic occurrence frequency of the estimation errors. Thus, this paper contributes to providing the improving QoS for CWA at low costs.

Insufficient illumination makes driver face detection at night challenging. This paper proposes an adaptive attenuation quantification retinex (AAQR) method to enhance the details of nighttime images. There are three phases in this method: attenuation restriction, attenuation prediction, and adaptive quantification. The performance of the proposed method was evaluated by employing a robust face detection method via sparse representation. The collected driver face images at night were categorized into three groups (up-down, left-right, and mixed) according to the illumination distribution in each image. Results have shown that the detection rates of the images enhanced by the proposed AAQR method were 82%, 84%, and 91% for the up-down, left-right, and mixed illumination groups, respectively. In comparison to other image enhancement methods, the detection rate of the AAQR method was 2%-36% greater. Furthermore, the mean computing time for a single 640 × 480 nighttime image using AAQR was less than most of the compared advanced methods. Thus, the AAQR method is recommended for applications in driver face detection tasks at night.

Detection of road or lane is indispensable for the environmental perception of advanced driver assistance systems. It has been an active field of research with a wide application prospect. However, due to the complex illumination and interferences, such as vehicles and shadows in the real driving environment, lane detection is still a challenging task today. To address these issues, a robust method for road segmentation and lane detection based on a normal map is proposed. The first step of this approach is to generate the normal map by using the depth information and then extract a segmented road pavement without vehicles and buildings based on the normal map. Second, we improve an adaptive threshold segmentation method and denoising operations to enhance the lane markings. Third, the combination of Hough transform and vanishing point makes it more accurate to determine the starting points of host lanes, and then the lanes in the following image sequence can be detected in the adaptive region of interest. Compared with the state-of-the-art methods, the experimental results on the data sets in two countries demonstrate that our approach produces more credible performance under various light conditions or dense traffic.