Many vehicles today include safety features that assist drivers in specific circumstances, such as keeping us from drifting out of our lane or helping us stop in time to avoid a crash or reduce its severity. If you're currently shopping for a new vehicle, review NHTSA's 5-Star Safety Ratings to make informed decisions about the safety features in your new vehicle. Vehicles are tested by the companies that build them. Companies must comply with Federal Motor Vehicle Safety Standards and certify that their vehicle is free of safety risks.
Many companies today are testing advanced automated vehicles to ensure that they operate as intended, but a great deal of work remains to be done to ensure their safe operation before they are made publicly available. Cybersecurity is a critical issue that DOT and automotive companies are working to address for the future safe deployment of these technologies.
Advanced vehicle safety technologies depend on an array of electronics, sensors, and computing power. In advancing these features and exploring the potential of fully autonomous vehicles, DOT and NHTSA are focused on cybersecurity to ensure that these systems work as intended. These are among many important questions beyond the technical considerations that policymakers are working to address before automated vehicles are made available. We are still many years from fully automated vehicles becoming available to the public. Department of Transportation issued the Federal Automated Vehicles Policy which set forth a proactive approach to providing safety assurance and facilitating innovation.
Building on that policy and incorporating feedback received through public comments, stakeholder meetings, and Congressional hearings, in September , the agency issued, Automated Driving Systems: A Vision for Safety 2. The updated guidance, 2. It also provides technical assistance to states and best practices for policymakers regarding ADS. In October , U. The U. DOT sees AV 3. The guidance is intended to be flexible and to evolve as technology does, but with safety always as the top priority. Skip to main content.
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United States Department of Transportation. Report a Problem. Toggle navigation Homepage. Vehicle-to-Vehicle Communication. Automated Vehicles for Safety. Safety Facts. Number of people killed in motor vehicle crashes in The Topic. Related Topic.
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Guidance Resources. Automakers aim to future-proof their brands through a personalized user experience. As the dominance of the driver-centric car recedes, automakers face new players, ideas and technologies offering passenger-centric, individualized mobility. Automakers aim to protect their brands through a personalized user experience. Digital displays are rapidly becoming a crucial part of the human-machine interface in vehicles.
But are there limits to this visual revolution? As an integral part of advanced driver assistance systems in connected cars or as a provider of intelligent and personalized infotainment in autonomous vehicles, automotive screen time has vast potential. The principles of individualized and shared seem contradictory. But technological advances can turn any connected car into your own personal mobility space. Cloud computing, 5G mobile communications and advanced display technology offer the key to providing a secure sense of individuality and ownership in a sharing economy.
The principles of individualization and shared seem contradictory. But technological advances are enabling portable personalization that can turn any connected car into your own personal mobility space. How can automakers prosper in the digital era? Xuleeo Hi there, sorry to hear what happened. BStosic We hear you. You could sign up for the JBL Newsletter or continue following us for news and updates to keep yourself in the loop. How can CIOs accelerate innovation in the rapidly changing automotive industry?
How can the automotive CIO be a force for change? Discover more. Is the answer to improved vehicle safety in the crowd?
Will cars need an ocean of antennas? How important will networks be in future? How will we be entertained in the autonomous age? How does my vehicle know before I do?
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Can hackers take control of my vehicle? How will I communicate with my car in future? Why should vehicles watch their drivers? Can the connected car keep up with its driver? However, the computing power of ECU has been notably increasing to process enormous real-time tasks in the most recent vehicular system [ 19 ]. In this paper, an intrusion detection system using the deep neural network DNN structure [ 37 ] is proposed to secure the in-vehicular network, e.
CAN network. The proposed technique trains high-dimensional CAN packet data after the dimension reduction to figure out the underlying statistical properties of normal and attack packets, and, in defense, it extracts the corresponding features to identify the attack. DNN has been shown to be effective for classifying statistical patterns and mapping complex non-linear input-to-output relations in various research fields such as artificial intelligence, multimedia processing, security [ 37 — 40 ] as well as in intelligent vehicular systems [ 41 — 44 ].
Our work is the first to employ the deep learning structure in the IDS of in-vehicular networks, which differs from earlier ANN-based intrusion detection methods [ 34 , 35 ]. Specifically, we use unsupervised deep belief network DBN pre-training methods [ 45 ] to efficiently train the parameters initializing the deep neural network. The parameters are tuned later to achieve a better classification result with the supervised learning. Experimental results demonstrate that the proposed method yields a superior performance in terms of a classification error with little computation complexity in the decision.
CAN is designed for half-duplex and high-speed broadcast bus in-vehicular network, providing the communication rate up to 1Mbps [ 18 ]. The CAN protocol is widely used in automotive manufactures as the de factor standard. In the protocol, each ECU broadcasts a message to the network using a data packet. Thus CAN packet has no explicit destination field.
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Fig 1 shows the syntax of the CAN data packet. The arbitration field offers two functions: 1 prioritizing a message by the ID in the decreasing order and 2 enabling each ECU to filter an interesting message. The ID field is used for a collision avoidance algorithm in the bus, which is extended to 29 bits later. The control field contains the size of the data field.
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The cyclic redundancy check CRC field detects any error in the data packet. The acknowledgement field confirms the receipt of a valid CAN packet. Intrusion detection techniques have been actively studied to help the conventional network resist malicious attacks. In literature quite a number of the intrusion detection techniques are developed based on machine learning techniques, based on the assumption that the patterns of the attack packets differ from those of the normal packets. In [ 34 — 36 ] artificial neural networks ANN and support vector machine SVM are applied to the intrusion detection, using a statistical modeling on a packet data.
The aforementioned works are based on supervised machine learning techniques, and, thus a number of labeled data sets are required in the training. As compared to the approach, Kayacik et al. Fig 2 shows a common architecture of the IDS based on machine learning.
The IDS includes various modules for gathering and analyzing a large amount of data packets. Typically, the monitoring module detects a type of an incoming packet after feature extraction. The profiling module contains the features trained off-line. If the monitoring module identifies a new attack type, the profiling module may update the database of the profiling module for upcoming packets. Deep learning refers to a machine learning technique using an architecture comprising a number of hierarchical layers of non-linear processing stages.
The architecture can be categorized into two types, i. The discriminative deep architecture provides abilities for pattern classification with the supervised learning as in the conventional feed-forward artificial neural networks ANN. The deep structure, namely, deep neural network DNN can be augmented with multiple hidden layers from the ANN structure. However, the augmented neural networks are inefficiently trained using the back-propagation learning with a gradient descent optimization due to the vanishing gradient problem [ 48 ].
In the backpropagation, the gradient of the error surface is computed in each layer while the gradient exponentially decreases with the number of the layers, thus causing a extremely slow convergent speed. To prevent the problem, the generative deep architecture characterizing the correlation of the observed data and the associated classes is used for initializing parameters of the discriminative architecture [ 49 ], called the unsupervised pre-training scheme.
In [ 49 ], the weight parameters interconnecting nodes in adjacent layers are efficiently trained using a top-down approach by considering the nodes as restricted Boltzmann Machines RBM. After the pre-training, fine-tuning is performed using the gradient descent method with the supervised learning as in the conventional feed-forward ANN [ 50 ].
The deep belief networks DBN [ 45 ] as a probabilistic generative model include several layers of stochastic hidden units on top of a single bottom layer of observed data to efficiently solve the vanishing gradient problem [ 49 , 50 ]. The DBN structure is shown in Fig 3 a where the top-two layers contain undirected connections, and the lower layers contain directed connections to the layers below. In this top-down manner, the weight vector w n is generated to form the visible data vector v , and the set of w n is used for initializing the parameters of the proposed classifiers later.
The solution is used similarly for many practical applications [ 41 , 43 , 51 ] using the DBN learning structures, and, therefore adopted in the proposed technique to pretrain the parameter as well. The proposed intrusion detection system considers a general type of an attack scenario where malicious data packets are injected into an in-vehicle CAN bus. The proposed intrusion detection system monitors broadcasting CAN packets in the bus and determines an attack, as shown in Fig 4. Our IDS design consists of two main phases, i.
The training phase is performed off-line as the training is time-consuming. In the training phase a CAN packet is processed to extract a feature that represents a statistical behavior of the network. Each training CAN packet has its binary label, i. Thus the corresponding features are expected to represent the label information.
We adopt the DNN structure to train the features, in which the weight parameters on the edges connecting the nodes are obtained. The detection phase is also shown in Fig 5. The same feature is extracted from an incoming packet through a CAN bus, and the DNN structure computes with the trained parameters to make the binary decision. The learning structure should be configured for the supervised learning as the DBN model in Fig 3 a provides unsupervised learning mechanism.
To this aim, the final classification layer including label information is added to the top layer of the DBN model to construct the discriminative deep learning structure. Fig 3 b shows the modified structure into the deep feed-forward ANN structure where the structure is trained with the bottom-up supervised learning manner, owing to the label information y.
It is highlighted that the weights w i in the hidden nodes of the DBN structure are obtained from the unsupervised pre-training at first. However, the parameters are used only for initializing the weights, and, they are fine-tuned by using the gradient descent method in the deep feed-forward ANN structure later. The feature is designed by considering computational efficiency.
In other words, the feature is extracted directly from a bitstream of a CAN packet so that the decoding is not necessary during the extraction. The occurrences of bit-symbols in a data packet are taken into an account. Otherwise, it is mapped to 0. All the bit positions in the DATA field may be used for generating the feature. However, the dimension can be reduced by considering specific semantics in the corresponding syntax element. The proposed technique regards mode information and value information according to the semantics. The mode information represents a command state of an ECU, for example, controlling wheels, and the value information represents the value of the mode, for example, the wheel angle or the speed, as shown in Fig 6.
The mode information is constant in a short period, while the value information may change with some noises. In the proposed technique the value information is only used for the training phase. The usage of the mode information will be shown in the detection phase. Denote p v is the data vector reduced from p. The learning mechanism of the proposed DNN structure to classify a normal packet and an attack packet is explained.
Fig 7 shows an input layer, multiple hidden layers, and an output layer. The feature vector is inputted to the input nodes of the structure. Each node in Fig 7 computes an output with an activation function using rectified linear unit ReLU , and the linear combinations of the outputs are linked to the next hidden layers.
In the learning phase, the input feature v goes through the visible nodes at the bottom of the neural network structure, in which initial weights are given by the DBN learning. Then, the weight vectors are fine-tuned in sequel. For this, we minimize a cost function C given as the mean squared error function between the prediction value and the output: 4 where w is the set of the weights in the network to be trained, y is the label, and h w v is a hypothesis function yielding an estimated output.
The class of a testing CAN packet is predicted in the detection phase. The output is computed with the trained weight parameters and the feature set extracted from the testing CAN packet as in the training. The classifier provides the logistic value 0 or 1, telling if the sample is normal packet or the attack packet, respectively. There can be a number of attack scenarios considered in an ECU, and the weight vectors can be trained fitted to each scenario.