Vehicle Controller Area Network Bus Research Articles

Lightweight Intrusion Detection Method of Vehicle CAN Bus under Computational Resource Constraints

In order to improve the security protection performance of the vehicle Controller Area Network (CAN) bus, the research builds an adaptive lightweight intrusion detection algorithm based on the limited computing and storage resources of the on-board ECU environment and the message cycle characteristics to supervise and detect the vehicle CAN bus intrusion. The results showed that the message cycle-based adaptive intrusion detection algorithm had high accuracy and recall rate, and fast computational search efficiency, with a stable detection time of less than 3 seconds. The intrusion detection capability is continuously optimized as the training time increases, and after stabilization, the resource utilization rate reaches over 95% with a throughput of 100Mb/s. The algorithm has strong protection capabilities. The average vehicle CPU usage of the algorithm is only 4.76%, which is 10.17% lower than the intrusion detection algorithm based on support vector machines. It can effectively prevent interference with the normal operation of the vehicle CAN bus. The algorithm has high detection accuracy for interrupt type attacks, and there are no false positives or missed alarms. For injection type attacks, the probability of missed alarms is less than 1%. The intrusion detection of vehicle CAN bus based on the message cycle characteristics provides technical reference for the safety and stability of the vehicle network, and has important practical value for the intelligent and networked development of the automobile industry.

Reinforcement Learning as a Path to Autonomous Intelligent Cyber-Defense Agents in Vehicle Platforms

Technological advancement of vehicle platforms exposes opportunities for new attack paths and vulnerabilities. Static cyber defenses can help mitigate certain attacks, but those attacks must generally be known ahead of time, and the cyber defenses must be hand-crafted by experts. This research explores reinforcement learning (RL) as a path to achieve autonomous, intelligent cyber defense of vehicle control networks—namely, the controller area network (CAN) bus. We train an RL agent for the CAN bus using Toyota’s Portable Automotive Security Testbed with Adaptability (PASTA). We then apply the U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory’s methodology for quantitative measurement of cyber resilience to assess the agent’s effect on the vehicle testbed in a contested cyberspace environment. Despite all defenses having similar traditional performance measures, our RL agent averaged a 90% cyber resilience measurement during drive cycles executed on hardware versus 41% for a naïve static timing defense and 98% for the bespoke timing-based defense. Our results also show that an RL-based agent can detect and block injection attacks on a vehicle CAN bus in a laboratory environment with greater cyber resilience than prior learning approaches (1% for convolutional networks and 0% for recurrent networks). With further research, we believe there is potential for using RL in the autonomous intelligent cyber defense agent concept.

Mass estimation of ground vehicles based on longitudinal dynamics using IMU and CAN-bus data

This paper considers the problem of real-time estimation of ground vehicle mass, based on longitudinal dynamics. Compared to previous contributions, this work relies on basic data from the vehicle controller area network bus. This allows for application to a very broad range of vehicles, by not using vehicle specific parameters such as current gear indicator and engine torque. The current gear ratio is predicted based on the engine and vehicle speeds. The engine torque is modelled by the mass air flow rate of the engine. The road gradient is estimated with an inertial measurement unit, with fusion of gyroscope and accelerometer using a complementary filter. To handle prolonged dynamic conditions affecting the performance of the filter, compensation of external accelerations is made by use of the vehicle speed from the controller area network bus. The mass is estimated with a recursive least squares filter with forgetting factor. The model is validated experimentally with data obtained from test drives with two different petrol powered passenger cars, equipped with a manual transmission and dual clutch automatic transmission respectively. The test drives consist of various driving conditions with different vehicle loads. The resulting mass estimates are generally good for both vehicles with variations within ±5% of the actual masses.

PASSENGER GROUND VEHICLE LIVE PARAMETER MONITORING AND GOVERNING USING AUTOMOTIVE IVN PROTOTYPE MODEL

Now a day the ground passenger vehicle safety becomes the most sensitive and major research area. There are many distributed sensors placed inside these vehicle for continually monitoring and actuating various moving and non-moving components of the vehicle. Thus in-vehicle based modern networking bus used to communicate data between various distributed sensor and actuator nodes, This paper describe the novel efficient implementation between Electronic control unit (ECU) and the distinguished CAN (controller area network) protocol utilized across most of the automotive industry, designed and maintained by Robert Boschs. The CAN based ECU system is implemented using three PIC microcontroller boards. The system is explain in simple way to understand the most difficult part of passenger vehicle. Real time system is prototypes and simulates within controlled environment, tested with all the possibilities and presumes conditions focusing towards all the real-world conditions. The system having multiple displays and indicators to indicate various parameters. The observations are analyzed and found satisfactory; the in vehicle network (IVN) is wider and sensitive subject which is studied extensively by most of the automotive abd allied industry. This research paper is the novel resemblance of the in vehicle CAN bus network in prototype presentation.

Accuracy and Characteristics of 2012 Honda Event Data Recorders from Real-Time Replay of Controller Area Network (CAN) Traffic

Prior EDR testing methodologies required setting events in the airbag control module in the vehicle during controlled driving behavior. Duplicating events was nearly impossible, and it was difficult to separate how much differences in recorded speeds to reference speeds was due to measurement error, wheel slip, reporting time delays, or data truncation within the EDR. Recording thresholds have also increased making non-deployment and deployment events closer in magnitude, increasing the risk of accidentally exceeding the deployment threshold while setting events. The new methodology eliminates the risk of accidentally deploying airbags while gathering GPS and CAN bus data in the test vehicle. The techniques presented in this paper also allows gathering of data in vehicle without tampering with the airbag control module, which reduces the potential liability to testers using rental or borrowed test vehicles. The new methodology allows for repeatable testing and mapping the transfer function between the vehicle CAN bus data and the EDR. Should a manufacturer make a design change to an ACM EDR, identical inputs can be given to exemplar ACM's from before and after the changes to document any change in the transfer function. This methodology allows researchers the ability to re-create events of interest in a low-cost, repeatable manner. The accuracy of the 2012 Honda CR-V and 2012 Honda Civic event data recorders were tested using this new two part methodology. First, the test vehicles were instrumented with both a Racelogic VBOX differential GPS speed measurement system and a Vector CAN data logger. The measurements from the VBOX were transmitted onto the vehicle's Controller Area Network (CAN) bus that also contained messages reflecting indicated vehicle speed, brake status, accelerator pedal position, steering wheel angle, individual wheel speeds and other signals. This put the GPS speed data on the same time base as the vehicle CAN speed signal such that no additional synchronization was required. This permitted analysis of the accuracy and update rate of the vehicle speed CAN signal, which is the source for speed data used in the Event Data Recorder (EDR). Second, a system was developed to replay the recorded CAN data to an exemplar airbag control module in the laboratory, such that the exemplar was receiving data exactly as if it were in a moving vehicle. A pneumatic fixture with a slide was built to allow the exemplar module to be accelerated to nearly 10 km/h (6 mph) and then stopped in about 80 msec. to create a non-deployment event that met the minimum 5 mph delta-V over 150ms threshold. Actuation of the event setting fixture was computer controlled (using LabVIEW) and synchronized with the CAN replay system so that the desired test condition could be replicated precisely. An external accelerometer was mounted to the airbag control module to mark when the ACM began recording and the accelerometer data was matched to the acceleration and delta-V data in the EDR. The desired test conditions were replayed to the airbag control module and a series of non-deployment events were set. Each event on the EDR data was read using the Bosch Crash Data Retrieval system. The EDR data was compared to the inputs, and it was determined that the two byte vehicle CAN bus signal was truncated to the next lower whole km/h when recorded in the EDR. Under steady state conditions the speed data was accurate within 2%. The vehicle CAN signal published new values every 0.1 seconds, and the CR-V updated values every 0.1 seconds, but the Civic only updated every 0.6 seconds during hard brake events. During hard ABS braking events the expected wheel slip led to the vehicle speed signal under-reporting ground speed, but the delay in the CAN bus update rate resulted in over-reporting ground speed in many of the tests. Language: en

Cooperative Adaptive Cruise Control Implementation of Team Mekar at the Grand Cooperative Driving Challenge

This paper presents the cooperative adaptive cruise control implementation of Team Mekar at the Grand Cooperative Driving Challenge (GCDC). The Team Mekar vehicle used a dSpace microautobox for access to the vehicle controller area network bus and for control of the autonomous throttle intervention and the electric-motor-operated brake pedal. The vehicle was equipped with real-time kinematic Global Positioning System (RTK GPS) and an IEEE 802.11p modem installed in an onboard computer for vehicle-to-vehicle (V2V) communication. The Team Mekar vehicle did not have an original-equipment-manufacturer-supplied adaptive cruise control (ACC). ACC/Cooperative adaptive cruise control (CACC) based on V2V-communicated GPS position/velocity and preceding vehicle acceleration feedforward were implemented in the Team Mekar vehicle. This paper presents experimental and simulation results of the Team Mekar CACC implementation, along with a discussion of the problems encountered during the GCDC cooperative mobility runs.