(NHTSA)  tallied 32,166 traffic deaths and estimated 1,715,000 injuries. Those 6.9 million crashes in 2015 incurred approximately $242 billion in economic costs. The NHTSA survey of the National Motor Vehicle Crash Causation Survey conducted from 2005-2007 (the most recent of this labor intensive and detailed type of study) attributed the critical cause of 94% of accidents to human driver error.  In order to improve the safety of vehicular transport, autonomous vehicles are expected to be trained better than humans and correct for most of these human driver errors. And for safety reasons among others, autonomous vehicles have made great strides in commercial investment  and technical capability . However, likely due to well publicized accidents including autonomous vehicles, polls in 2018 show a downturn in consumer trust in autonomous vehicles from the prior two years . As consumers recognize the difficulty for a computer to understand and navigate the ambiguous situations of the roads, computer scientists and automotive engineers grapple with the shortcomings of autonomous vehicles. These shortcomings include difficulty in recognizing and giving right of way to human vehicles in merge situations , recognizing and avoiding pedestrians and other legged creatures , and locating and avoiding stationary objects . So despite how advanced they are currently, autonomous vehicles have weak spots operating in real world scenarios.
(PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a 17 node charging network equipped with Level 2 chargers at a major North American University campus. The data is recorded for
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- One of the main concerns for the security of in-vehicledata is spoofing messages on the in-vehiclenetwork. Controller Area Network (CAN) is the most extensively embedded network protocol in vehicles. In the last decade, security attacks in vehicles have been increasing and have been reported in several papers. Therefore, security measures are expected that meet the requirements of real time and cost constraint for in-vehicle control network. In this paper, we propose centralized authentication system in CAN with improved CAN controller. Our experimental results demonstrate that our proposal method is effective on real in-vehiclenetwork environments.
From all the data gathered by the vehicle acquisition system statistics of each and every inch of road can be generated and updated to the wireless network server. Also in this system a vehicle’s dynamics and stability plays a crucial role, determining the vehicle’s stability and limits for the approaching road. Factors like Load transfer, lateral acceleration, suspension setup, aerodynamics etc., are some of the deciding criteria in determining the attitude of the vehicle. The road information and the vehicledata and statistics can be put in an algorithm and the critical velocity can be computed. All these information then can be displayed to the user and thus he is warned about the vehicles limit for the road ahead, so that he can take necessary action to avoid a crash. The information will be displayed on the display system according to the speed of the vehicle, if the
The unmanned Ground Vehicles are useful in military, surveillance, riot control etc . Based on the results of our simulation, it is inferred that the models results were incompatible becauseof embodiment of different variables and different variables. Hence, this brings us to a conclusion that one specific pathloss model is not ideal for all antenna heights for all terrains .However, Cost 231 WI model could be favoured owing to its suitability to our scenario . Cost 231 WI model is the best suited path loss model for our project and a combination of star and mesh topology would give best results for our scenario.The above choices may give best results for our case but different scenarios might need different choices of path loss models, network technology depending upon the amount of traffic , fading and shadowing and whether the communication is LOS or NLOS.In order to validate the productiveness of the proposal ,various remote operating tests for the UGV were conducted. The UGVs are controlled from a remote base station which receives real time data and sensor information from the UGVs and in return sends movement and motion commands.In some developed systems,a graphical user interface reduces the workload on the operator and the operator can control multiple unmanned ground vehicles simultanouly.As a consequence, we expect a UGV capable of playing an inevitable role in the military operations in near future.
Drones have aroused great interest in environment monitoring, data collection and device transport [1–3]. Because of their capabilities of rapid manoeuvring, great mobility, and precise hovering, unmanned quadrotor helicopters have been deployed for missions in environments unreachable by humans [4,5]. New applications have continuously appeared in recent publications, magazines and newspapers [6–8]. Multiple linear and nonlinear control systems have been devised to achieve trajectory planning, obstacle avoidance, UAVs’ cooperation, lifting and landing control [9–11]. One of these applications is the UAVs’ aerial transportation systems . They can deliver different equipment and other urgently-needed devices to remote areas. However, in real complex situations, the quadrotor UAVs face many control problems with respect to external disturbances. As shown in [13–15], various research works have been conducted on the quadrotor transportation system in the past few years. The typical solution is holding a payload by the actuators with which it is equipped [16,17]. However, this will bring about the slow reaction problem due the inertia added to the UAV. In order to retain the good manoeuvrability of UAVs, another solution is proposed by attaching the payload to the transportation platform via a cable [18,19]. This approach has been widely used in the transfer of radioactive substances or large cargoes . Therefore, the study of quadrotor transportation systems is of theoretical and practical importance. In particular, the robust control of the quadrotor with uncertainties and delays is a critical problem both for the platform and humans on the ground.
The project has been studied and designed using raspberry pi. The main benefit of the present system is power saving. This initiative will help the government to save this energy and meet the domestic and industrial needs. In addition to energy consumption, another advantage it provides less maintenance cost. This project is cost effective, practical, and the safe to travel at night time. According to statistical data we can save more electrical energy that is now consumed by the highways. We have implemented a prototype of this system. The proposed system is especially appropriate for street lighting in city and rural areas where the traffic is low at night times. Independence of the power network permits to implement it in remote areas where the classical systems are prohibitively expensive. The system is versatile, extendable and totally adjustable to user need.
Data collection for driver monitoring has many challenges, including syn- chronising data streams and deriving a ground truth. In this thesis three forms of ground truth have been used, road type, driver distraction status, and heart rate. Each of these describes the driver state in different ways, from the driving environment to their physiology. It remains an open question in the driver monitoring field which kinds of ground truth best describe cognitive workload. Physiological measures are often considered to be best (e.g. [23, 57, 99, 161]), but require personalised baselines to determine changes in driver status and often require invasive tools to cap- ture the data. Once data is captured, synchronising the streams to other data sources, such as CAN-data is also a challenge to this research, which complicated systems based on Global Positioning Satellites (GPS) or syn- chronising pulses are often used to overcome. A simplified and standard mechanism is therefore required in future, to enable accurate capture of data in ever more realistic scenarios.
• Sensor based detection: Muter et, al.  proposed anomaly detection using sensors. These sensors are designed based on the network protocol specifications, redundant data sources in the vehicles and the defined cooperative networking behavior of the devices. They do not produce false positives. Since they are based on unambiguous and reliable information. Limitation: There is a high possibility of false negatives in this case. The errors caused by hardware will also be classified under attack.Also, the source of anomaly cannot be detected (hardware error or malicious attack) using sensors. Moreover, the attacks caused by messages that are fully compliant to the networks normal behavior will be left undetected.
and feet. When horizontal flip operation is applied, it makes no sense to consider width partition. Unlike the human body, a car body can be roughly divided into ceiling, wind- shield, header panel, wheels, etc. on the vertical axis, and into hood, doors, trunk, etc. on the horizontal axis. Thus, we employ both height-wise and width-wise partitions in our model for vehicle Re-ID. Moreover, filters in a convo- lutional layer generate channel information. Even though inputs are the same, they learn and update their parame- ters independently. Local features on channel-wise parti- tioned parts can be different from global features. By 3D partitions, our proposed PRN is able to extract maximally distinct local features, which helps to build robust vehicle appearance signatures.
The neuralnetwork used is multi layer feed-forward network with back propagation learning algorithm and is designed using MATLAB programming environment. The employed configuration contains 3 neurons in the input layer, 6 in the hidden layer and 4 in the output layer as shown in Fig. 4. The numbers of neurons in hidden layer are selected on trial and error basis. Three inputs to the neuralnetwork are distance information from 4 sensors. Centre and back sensors are combined to form one input while other two inputs are from left and right sensors. The outputs from the neuralnetwork are direct commands for motors. The activation function used for hidden layer is tangent-sigmoid function while pure linear function is employed in output layer. The output of a neuron can be expressed by the equation:
In this project it is planned to use data logging in vehicle system monitoring. Now-a-days in cars, bikes, or any other vehicles the faults are analysed only by the trained technicians (Mechanics). So, the vehicle user only know the outline of the problem and also the user doesn't know when the vehicle have to be serviced or which part of the vehicle is failed or malfunctioning. While driving the vehicle a particular device is malfunctioning in that time user doesn’t know the effect of improper functioning, the user came to know only when the device stops working. To educate the vehicle users and also to provide interface between user and the vehicle this data logging concept is used.
Abstract: In current timing of real world the traffic congestion and control is the major issue in the different cities. Real time traffic control is a main criterion of the urban traffic signal control system, and giving viable ongoing traffic signal control for a substantial complex traffic system is a testing issue. The main objective of the research work is to find and adjust the timing of signals based on the traffic density to overcome the congestion. Such a situation arises in a city where outbound vehicles during morning time and inbound vehicles during evening time is more while the vehicular movement in the opposite direction is less. To do this, the paper proposed the hybrid technique of neuralnetwork and ant colony optimization to overcome the error ratio and improve the accuracy of traffic congestion. And further, the paper proposed to find out the shortest path for the vehicles that are struck into the traffic.
An artificial neuralnetwork (ANN), usually called neuralnetwork (NN) is a data processing system consisting of a large number of simple and highly interconnected processing elements (artificial neurons) inspired by the structure of the cerebral cortex of the brain (Lefteri, H.T. and Robert, E.U., 1997). ANN is a type of artificial intelligence that attempts to imitate the way of human brain works (Sivanandam, S.N. et al., 2011). Basically, neuralnetwork deal with cognitive tasks such as learning, adaptation, generalization and optimization. Certainly, recognition, learning, decision making and action represent the principal navigation problems (Janglova, D., 2004). Neural networks perform two major functions that are learning and recall. Learning is the process of adapting the connection weights in an artificial neuralnetwork to produce the desired output vector in response to a stimulus vector presented to the input buffer. Recall is the process of accepting an input stimulus and producing an output response in accordance with the network weight structure (Lefteri, H.T. and Robert, E.U., 1997). Learning rules enable the network to gain knowledge from available data and apply that knowledge to assist a manager in making key decisions. Neural networks also able to compute any computational function. It also can be defined as parameterized computational nonlinear algorithms for data, signal and image processing (Sivanandam, S.N. et al., 2011).
For the sake o f space, o n ly the m ost cru cia l system perform ance o f each case is discussed in d e ta il, w h ic h is in the s p lit-p co n d itio n . It is also noted th a t a ltho ug h the V D C system succeeds in m a in ta in in g the vehicle s ta b ility , the calcu la tio n results show n fo r the n o m in a l la te ral acceleration and b o d y s lip angle are the least accurate com pared to the oth e r sim u la tio n results. This is because the s p lit fric tio n c o n d itio n (p = 0.2L/0.6R ) creates the greatest vehicle in s ta b ility due to the tra c tio n lim it d is p a rity betw een the le ft and rig h t tires o f the vehicle. This d is p a rity creates the largest yaw m om ent im balance fo r the vehicle. A p p e n d ix E contains a d d itio n a l sim u la tio n results o f a h ig h road fric tio n co n d itio n , w here the lin e a r bicycle m odel responses are v e ry close to the no n -lin e a r CarSim results. This indicates th a t the bicycle m odel is a good estim ator o f the n o n -lin e a r system response w here the road coe fficien t o f fric tio n is h igh .
However the issue of the mechanisms of child pedestrian collisions outside driveways has not been well evaluated. In a few hospital based studies, there was not enough information regarding the crash characteristics. In order to answer the critical questions regarding the differ- ences in children injury profile among passenger vehicle, LTV and van-involved collisions, further studies with larger sample size are required. In spite of these limitations, the PCDS provides a solid opportunity for researchers to evaluate the effects of vehicle front end design on pedestrian injuries. In the PCDS, the risk of pedestrian death for LTVs was 3.4 times that for passenger vehicles. Other studies evaluat- ing pedestrian mortality in real world crashes with different classes of vehicles are scarce. However, in general the literature corroborates the findings of the current study. Lefler and Gabler in an analysis of three data sets (Fatality Analysis Reporting System, General Estimates System, and PCDS) reported that one fourth of the large van-pedestrian crashes, one out of seven sport utility vehicle-pedestrian crashes, and one out of 20 passenger vehicle-pedestrian crashes result in death. 4 27 Analysis of 217 mortality cases in
vehicle in order to avoid dangerous rollover. One of the most important task in vehicle control is to preserve yaw stability during cornering in order to avoid skidding of the vehicle. In the research papers and industrial practice differential braking is the most popular way to realize yaw stability of the vehi- cle, although this can be achieved by active torque distribu- tion (Zhang et al.) and active steering (Anwar et al.) as well. Integrated control that cooperates four wheel steering and yaw moment control to improve the vehicle handling performance and stability has also been researched (Jianyong et al., Wang
While using the electric vehicles the operating cost of the vehicle can be reduced. It can be made as cost effective. Due to the impact of increase in the fuel price, it is difficult among the public to use internal combustion engine vehicles. The future of automobile is going to be the era of electric vehicles. Thus the existing internal combustion vehicles cannot be demolished. The pollution emitted by the automobiles is increasing rapidly nearly about 73% of total pollution due to the usage of internal combustion engines. The newly manufactured electric bikes are higher in cost. The main advantage of the electric vehicles is reducing emissions. Electric vehicles use large battery packs for the energy which are higher in cost. They use lithium-ion batteries which prices around thousands. Obviously, everyone is looking for ways to make electric vehicles less costly. One way of doing this is to consider converting an internal combustion engine (ICE) vehicle into a new electric one. There are a number of kits on the market that can be utilized to do this conversion, but these kits costs higher. Electric vehicles consist of batteries for energy, an electric motor for power, a controller to control the flow of energy to the motor, and a potentiometer to allow accelerator pedal to provide input to the controller. The vehicle’s gasoline engine, exhaust system, petrol tank, and clutch assembly will no longer be needed. Electric vehicle conversion is the replacement of a vehicle’s combustion engine and connected components with an electric motor and batteries, to create an all-electric vehicle. Another option is to replace a large
Electric Vehicles (EVs), represents a new concept in the transports sector around the world . Consequently, the interest in technologies for EVs has significantly increased in the last years, resulting in several scientific publications concerning this subject . It is expected that the market share of EVs will exponentially grow comprising 24% of the U.S. light vehicle fleet in 2030, representing 64% light vehicle sales in this year . In this context, the EVs battery charging process (Grid-to-Vehicle, G2V) must be regulated  to preserve the power quality in the power grids . Nevertheless, with the proliferation of EVs a considerable amount of energy will be stored in their batteries, arising the opportunity of the energy flow in opposite sense (Vehicle-to-Grid, V2G) . In the future smart grids, the interactivity with the EVs will be one of the key technologies, contributing to the power grid autonomous operation . Nowadays, several projects related with smart grids are under development around the world . Regarding this new approach, especially in homes equipped with charging points for EVs, besides the G2V and V2G operation modes the EVs can also operates as voltage source capable to feed the home loads. This technology, begins to be denominated in the literature as Vehicle-to-Home (V2H). As example of this new approach, Nissan presented the “LEAF-to-Home” system. This
The data used in this paper was collected over 16 drives across the Midlands, UK, in two cars. Each journey involves at least one driver, with a mean journey length of 51 minutes. Output from 15 CAN-bus sensors, listed with brief explanations in Table 2, were recorded each at 20Hz for a total of 49403 seconds, which is comparable to the length of data used in (Huang et al., 2011). Some sensors used are expected to have very little relevance in determining the road type, and others are highly redundant. As previously stated, these expectations may be incorrect, as is the case with the ambient temperature signal. Although it may initially be expected to be a poor predictor, it has one of the higher MI scores (0.197 for carriageway type) in data we have collected. On further inspection we find that its Pearson correlation with vehicle speed, which is expected to be a good predictor, is 0.774. This makes some intuitive sense, as the temperature near the engine will rise with vehicle speed as the engine works harder. With this insight we can say that ambient temperature is a good predictor of road type, but that it is somewhat redundant to other signals. After signal and feature selection, only the features which are useful for the problem should be used in classification.