Controller Area Network (CAN) is the main bus that connects Electronic Control Units(ECUs) in automobiles. The CAN protocol has been revised over the years to improve vehicle safety but the security of communication over a CAN bus is still a concern. Despite different kinds of attacks challenge the CAN security, the attack that injects masqueraded CAN frames is extremely difficult to defeat given the limited resources available in CAN system. We propose a low-cost detection mechanism to address the masquerade and replay attacks on the CAN bus. Existing work either requires to store a long list of legal CAN IDs or uses hardware-consuming cryptographic algorithms to detect attacks. In contrast, our method only adds one more CAN ID to the acceptance filter of the CAN node under protection, eliminating the need for cryptographic modules and significantly reducing the hardware cost. We implemented our method in a CAN system prototype. Our experimental results show that the latency overhead of the proposed method is approximately three orders of magnitude less than that of other methods. Our method is capable of detecting the masqueraded and replayed CAN frames with a detection speed of 40μs, which satisfies the real-time requirement of automobiles.

By the nature of their jobs first responders have to interact with in-vehicle devices even as they drive under challenging road conditions. In this paper we assess the state-of-the-art in creating safe in-vehicle user interfaces for first responders, and we propose six research and development priorities for future work in this realm.

The Project54 mobile system for law enforcement developed at the University of New Hampshire integrates the control of disparate law enforcement devices such as radar, VHF radio, video, and emergency lights and siren. In addition it provides access to state and national law enforcement databases via wireless data queries. Officers using Project54 are free to inter-mix three different user interface modes: the device native controls; an LCD touchscreen with keyboard and mouse; and voice commands with voice feedback. The Project54 system was utilized by the New Hampshire State Police agency wide for a period of seven years spanning 2005 through 2011. This paper presents an analysis of user preferences in regard to user interface modes during the three years 2009 through 2011, obtained through logs of daily system use in approximately 200 police cruisers. Results indicate that most officers chose to use the touch screen controls frequently instead of the device native controls, but only a minority chose to use the speech command interface.

The Project54 effort aims to improve the ability of police to manipulate data in mobile units as well as to provide a way to seamlessly integrate all in-car electronic devices. Work is being done the integration of in-car hardware and software, user interface integration, and the integration of the cruiser into a wireless data network. The system is being tested in three New Hampshire State Police cruisers, USA. The entire New Hampshire State Police fleet of 250 cruisers will be equipped with the system.

Police cruisers have a variety of aftermarket equipment such as lights, sirens, radar units, radios, etc. Most police aftermarket equipment was not designed with integration in mind. In order to allow the integration of electronic equipment in police cruisers a common interface for connecting aftermarket car electronics to an intelligent transportation systems data bus (LOB) was designed and implemented. The interface hardware uses Version 2.0B of the control area network (CAN) protocol as the method of hi-directional data transmission on the IDB. As a means to connect the aftermarket devices to the IDB the interface has two options. The first type of connection offered is a serial port that uses standard RS-232 signaling. The second type of connection is a port that offers five parallel TTL level signals that can be used for a variety of applications including switching The common interface is currently being installed in several New Hampshire State police cruisers. Initial tests show the system to be reliable.

An adaptive dynamic balance scheme was implemented and tested on an experimental biped. The control scheme used pre-planned but adaptive motion sequences. CMAC neural networks were responsible for the adaptive control of side-to-side and front-to-back balance, as well as for maintaining good foot contact. Qualitative and quantitative test results show that the biped performance improved with neural network training. The biped is able to start and stop on demand, and to walk with continuous motion on flat surfaces at a rate of up to 100 steps per minute, with up to 6 cm long step.

The CMAC (cerebellar model arithmetic computer) neural network, an alternative to backpropagated multilayer networks, is described. The following advantages of CMAC are discussed: local generalization, rapid algorithmic computation based on LMS (least-mean-square) training, incremental training, functional representation, output superposition, and a fast practical hardware realization. A geometrical explanation of how CMAC works is provided, and applications in robot control, pattern recognition, and signal processing are briefly described. Possible disadvantages of CMAC are that it does not have global generalization and that it can have noise due to hash coding. Care must be exercised (as with all neural networks) to assure that a low error solution will be learned.>

A learning control technique that uses an extension of the cerebellar model articulation control network developed by J.S. Albus (1975) is discussed, and results of real-time control experiments that involved learning the dynamics of a five-axis industrial robot (General Electric P-5) during high-speed movements are presented. During each control cycle, a training scheme was used to adjust the weights in the network in order to form an approximate dynamic model of the robot in appropriate regions of the control space. Simultaneously, the network was used during each control cycle to predict the actuator drives required to follow a desired trajectory, and these drives were used as feedforward terms in parallel to a fixed-gain linear feedback controller. Trajectory tracking errors were found to converge to low values within a few training trials, and to be relatively insensitive to the choice of control system gains. The effects of network memory size and trajectory characteristics on learning system performance were investigated.>

The authors describe a cerebellar model arithmetic computer (CMAC) neural network and its use in learning the inverse function necessary for deconvolution and nonlinear inverse filtering. Simulations are described that use random noise, telegraph, or bit string signals as inputs to linear and nonlinear systems to generate the signal to be inverse-filtered. Results are shown for linear systems with decaying sinusoidal impulse responses and nonlinear systems with memory having saturating nonlinearities. Examples with low noise and testing (nontraining) results with new random sequences are shown. The results show considerable promise.>

A practical neural network-based learning control system is described that is applicable to complex robotic systems involving multiple feedback sensors and multiple command variables. In the controller, one network is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. A second network is used to learn to reproduce the nonlinear relationship between the system command variables and the changes in the video sensor outputs. The learned information from this network is then used to predict the next set of video parameters, effectively compensating for the image processing delays. The results of learning experiments using a General Electric P-5 manipulator are presented. These experiments involved control of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm, using moving objects with arbitrary orientation relative to the robot. No a priori knowledge of the robot kinematics or of the object speed of orientation relative to the robot was assumed. Image parameter uncertainty and control system tracking error in the video image were found to converge to low values within a few trials.>

A practical learning control system is described which is applicable to the control of complex robotic systems involving multiple feedback sensors and multiple command variables during both repetitive and nonrepetitive operations. In the controller, a general learning algorithm is used to learn to reproduce the relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is then used to predict the command signals required to produce desired changes in the sensor outputs. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables, facilitating control system modification for specific applications. The results of two learning experiments using a General Electric P-5 manipulator are presented. The first involved learning to use the video image feedback to position the robot hand accurately relative to stationary objects on a table, assuming no knowledge of the robot kinematics or camera characteristics. The second involved learning to use video image feedback to intercept and track objects moving on a conveyor. In both experiments, control system performance was found to be limited by the resolution of the sensor feedback data, rather than by control structure limitations.

A hardware system for performing video image stereo matching is proposed which utilizes phase-locked loop techniques to eliminate the intensive computations characteristic of many stereo matching algorithms. An initial evaluation of the technique is presented. The proposed hardware system is simulated in software on a VAX-II/730 minicomputer and is used to determine the disparity between a stereo pair of digitized video images. Potential hardware design problems and their possible solutions are discussed. The proposed stereo image matching technique promises to provide stereo disparity information at standard video frame rates using currently available integrated circuits.