Federated Learning (FL) is a technique that allows multiple participants to co-train machine learning models, while also enhancing privacy by avoiding the exposure of local data. However, it is important to note that despite its effectiveness, there is still a potential risk of leaking users’ private information through weight analysis during FL updates. Local Differential Privacy (LDP) is a technique used to prevent individual information leakage by adding noise to the user's model parameters. However, FL based on LDP lacks dynamic optimization and adaptation considering privacy and data utility, especially regarding noise constraints. This paper investigates FL under the scenario of noise optimization with LDP. Specifically, given a certain privacy budget, we design the adaptive LDP method via a noise scaler, which adaptively optimizes the noise size of every client. Second, we dynamically tailor the model direction after adding noise by the designed a direction matrix, to overcome the model drift problem caused by adding noises to the client model. Finally, our method achieves higher accuracy than some existing works with the same privacy level and the convergence speed is significantly improved.

An autonomous vehicle (AV) is a combination of subsystems, measuring its driving environments with different sensors (e.g., camera, RADAR, and LiDAR) in real time. AVs follow their control policies and make real-time control decisions based on sensor measurements to ensure driving safety. Control policies in an AV are usually implemented with codes and not open to the public and drivers, which results in people’s strong concerns about driving safety. In this article, we propose a data-driven control policy-based driving safety analysis system (PoSa) to analyze the driving safety of a target AV. In PoSa, we first build a data-driven control policy extraction method to extract control policies of a target AV based on its historical driving data. Then, we develop a hazard driving scenario identification method to identify possible hazard driving scenarios of a target AV by executing the extracted control policies under different driving scenarios. Finally, we use vehicle driving data from one industry-standard AV platform (Baidu Apollo) to evaluate PoSa’s hazard driving scenario identification performance. We compared its identification results with Baidu AV accident reports from California DMV and the hazard driving scenario identification results cover as many as 89% hazard driving scenarios in the Baidu AV accident report, which demonstrates that PoSa has good performance on identifying hazard driving scenarios and its identification results can be used to optimize control policies for driving safety improvement.

Pure electric vehicles (EVs) become more and more popular in current automotive markets. With the wide application of EVs in the market, more EVs will be charged in residential homes. An advanced metering infrastructure measures real-time electricity consumption of individual residential homes. However, the advanced metering infrastructure is exposed to attacks given the fact that its components (e.g., smart meters) are located at public places. Under this situation, electricity consumption information of an individual residential home with an EV can be easily stolen. In this article, we propose an EV trip information inference system (TIIS) to infer EV trip information (origin-destination of each driving trip in a day) of a residential home based on its real-time electricity consumption measurements in a day. To the best of our knowledge, this is the first driving privacy relevant work caused only by residential electricity consumption data. For a residential home with an EV, TIIS derives the relationship between trip numbers in a day and charging-starting time, and the relationship between total driving distance and total charging energy. To derive the EV trip information of a residential home, TIIS first extracts an EV charging profile from its electricity consumption data and identifies EV model type and trip numbers as well as total driving distance based on EV charging profiles. Then, TIIS infers EV trip information based on the identified information. We used electricity consumption data and EV driving data to evaluate inference performance of TIIS. The experimental results demonstrate TIIS has as high as 81% trip inference accuracy and works on residential homes with different EV model types.

With the advancement of dynamic wireless charging for Electric Vehicles (EVs), Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to MED deployment, are not directly applicable for city-scale EV-to-EV dynamic wireless charging. We presentMobiCharger:aMobile wirelessChargerguidance system that determines the number of serving MEDs, and their optimal routes. We studied a metropolitan-scale vehicle mobility dataset, and found: most vehicles have routines, and the number of driving EVs changes over time, which means MED deployment should adaptively change as well. We combine EVs' current trajectories and routines to estimate EV density and the cruising graph for MED coverage. Then, we develop an offline MED deployment method that utilizes multi-objective optimization to determine the number of serving MEDs and the driving route of each MED, and an online method that utilizes Reinforcement Learning to adjust the MED deployment when the real-time vehicle traffic changes. Our trace-driven experiments show that compared with previous methods,MobiChargerincreases the medium State-of-Charge of all EVs by 50% during all time slots, and the number of charges of EVs by almost 100%.

Previous methods for passenger demand inference are unable to capture the effect of all possible random factors (e.g., accident and weather), hence resulting in insufficient accuracy. Moreover, due to the lack of charging optimization, existing taxicab dispatching methods cannot be applied to electric taxicabs directly. We propose CD-Guide, which provides Charging and Dispatching Guide for electric taxicabs based on customized selection and training of historical passenger demand data, multiobjective optimization, and reinforcement learning (RL). By analyzing a large-scale electric taxicab data set, we found that: 1) the histogram of passengers’ origin buildings is effective in illustrating the suitability of historical data for learning; 2) passenger demands in different regions vary a lot due to various random factors; and 3) charging time must be considered in dispatching electric taxicabs. We first develop a passenger demand inference model based on customized selection and training of suitable historical passenger demand data. Then, we develop two taxicab guidance methods that utilize multiobjective optimization and RL, respectively, to maximize the taxicab’s likelihood of finding passengers, maximally prevent the taxicab from missing passengers due to charging, and, meanwhile, maintain the continuous service of the taxicab. Extensive experiments on real-world data sets demonstrate that compared with the state of the art, CD-Guide increases the total number of served passengers by 100%, and the minimum State-of-Charge of all taxicabs by 75% during all time slots.

For a modern vehicle, if the sensor in a vehicle anti-lock braking system (ABS) or controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and the driver’s life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate, and no method can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack (SA) and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation that considers the real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of attack) by minimizing errors between the predicted vehicle states and the measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from the measured vehicle states to generate correct vehicle states for a vehicle ABS. We conducted the first real SA experiments to show how a magnet affects sensor readings. Our simulation results demonstrate that our attack detection method can detect SA and CAN bus attack more accurately compared with existing methods, and also that our attack mitigation strategy almost eliminates the attack’s effects on a vehicle ABS.

Autonomous vehicles (AVs) follow their control policies and make real-time decisions based on measured signals from sensors to ensure driving safety. With the development of sensor measurement technologies, an AV usually adopts more sensors to measure its driving environments and develops more control policies to satisfy people’s expectations on driving safety. However, these control policies are usually implemented with codes that are not open to the public and usually difficult to understand for people, which causes people’s strong concerns about AVs’ driving safety. In this paper, we propose a control policy based driving safety system (Polsa) to help improve $\overline{\mathrm{d}\mathrm{r}\mathrm{i}}$ving safety of a given AV. For a given AV, Polsa extracts its control policies and determines the safest control behavior among multiple control behaviors for each given trigger condition, which can be used by AV companies that produce the AV to improve the AV’s driving safety. Accordingly, first, Polsa has a control policy extraction method that uses dynamic time warping and k-means clustering technologies to cluster historical driving data with the same control behavior type together and then analyzes positions and driving speeds in each cluster to extract control policies of a target AV. Second, Polsa has an optimal control policy determination method to determine the safest control behavior for each given trigger condition. Unlike previous works that consider that the state of the target AV’s nearby vehicle is constant during a time period, Polsa considers time-varying driving state of its nearby vehicle, thus deriving more safer control behavior. We use an industry-standard AV platform (Baidu Apollo) to evaluate optimal control policy success rate of Polsa in comparison with two state-of-the-art methods. The comparison results show that Polsa can extract control policies with 83% accuracy, and improve optimal control policy success rate by 28% compared with existing methods, which demonstrates high performance of Polsa in extracting control policies and determining optimal control behavior.

The battery pack in an electric vehicle (EV) usually contains thousands of single cells and these cells are connected together to generate the required voltage and capacity. Cells usually become aged after a certain number of charging and discharging processes and State-of-Health (SOH) of each cell needs to be monitored periodically to ensure normal work of EVs. However, SOHs of cells cannot be directly measured in practice. Besides, aging propagation phenomena between cells (cells in the same battery pack have different SOHs and the aged cell accelerates aging processes of other nearby cells) exists in a battery pack. In this paper, we propose a data-driven Battery Health Estimation System (BaHeS) to estimate SOH of each cell in a battery pack based on cell information (e.g., voltage and temperature). BaHeS firstly proposes a voltage entropy based detection method to detect abnormal cells by calculating voltage entropies of all cells in a time period. And then, BaHeS builds a cell aging propagation model to model the effect of the detected abnormal cell on its nearby cell by estimating SOH change. Lastly, BaHeS applies a Long Short Term Memory (LSTM) based neural network with the estimated SOH change and cell information as inputs to estimate SOH of each cell. We used battery pack usage datasets from total 50 EVs to evaluate SOH estimation accuracy of BaHeS. The experimental results demonstrate that BaHeS has good SOH estimation performance with as high as 93% accuracy and improves estimation accuracy by 18% compared with existing methods.

Electronic control units and actuators in a modern vehicle transmit messages through a controller area network (CAN) bus to ensure driving safety. However, adversaries can easily inject abnormal messages into the CAN bus through an external interface to affect vehicle driving safety. Existing abnormal message detection methods only detect abnormal messages, which are included in their training data and do not consider individual vehicle’s message transmission behaviors, which makes existing methods difficult to detect all abnormal messages accurately and results in their limited application in different vehicles. In this paper, we propose a neural network based abnormal message detection system (NaDS), to detect abnormal messages in the CAN bus. In NaDS, we firstly build a Long Short-Term Memory (LSTM) network to detect both known abnormal messages (included in the training data) and unknown abnormal messages (not included in the training data) in the CAN bus. In the LSTM network, we select message IDs and data field values as network inputs and use a generative adversarial network (GAN) based abnormal message generator to train the LSTM network. We then develop a transfer learning method to transfer a pre-trained LSTM network from one vehicle into a new LSTM network based on a small amount of training data to detect abnormal messages in another vehicle. We use real-vehicle CAN bus message datasets including three different vehicle types to evaluate abnormal message detection accuracy of NaDS. The experimental results demonstrate that NaDS can improve abnormal message detection accuracy by 29% compared with existing methods and keep high abnormal message detection accuracy during network transfer processes.

With the fast development of autonomous vehicle (AV) technology and possible popularity of AVs in the near future, a mixed-vehicle type driving environment where both AVs and their surrounding human-driving vehicles drive on the same road will exist and last for a long time. An AV measures its driving environments in real time and make control decisions to ensure driving safety. However, surrounding human-driving vehicles may conduct aggressive driving behaviors (e.g., sudden deceleration, sudden acceleration, sudden left or right lane change) in practice, which requires an AV to make correct control decisions to eliminate the effect of aggressive driving behaviors on its driving safety. In this paper, we propose a reinforcement learning based decision-making system (ReDS) which considers aggressive driving behaviors of surrounding human-driving vehicles during the decision making process. In ReDS, we firstly build a mixture density network based aggressive driving behavior detection method to detect possible aggressive driving behaviors among surrounding vehicles of an AV. We then build a reward function based on aggressive driving behavior detection results and incorporate the reward function into a reinforcement learning model to make optimal control decisions considering aggressive driving behaviors. We use a real-world traffic dataset from the United States Department of Transportation Federal Highway Administration to evaluate optimal control decision determination performance of ReDS in comparison with the state-of-the-art methods. The comparison results show that ReDS can improve optimal control decision success rate by 43% compared with existing methods, which demonstrates that ReDS has good optimal control decision determination performance.

For the situation where plenty of trust models are built to improve node cooperation in mobile peer-to-peer environments, node cooperation is analyzed in this paper. An Android APP is first developed to investigate users' satisfaction on cooperation degree based on their internal states, and then according to the investigation results, nodes' utility on cooperation degree based on internal states is designed. A node cooperation optimization model (COM) is built to maximize the utilities of all involved nodes and an intelligence algorithm is applied to solve this optimization problem. Because the results show that the cooperation degrees before and after optimization are very similar, each node can decide its cooperation degree only based on own information. Finally, the model is verified and applied to design a new routing protocol. The experiment results demonstrate that the proposed routing protocol can achieve higher routing success ratio and average node utility than other popular routing protocols.

For a modern vehicle, if a sensor in the vehicle Anti-lock Braking System (ABS) or the controller area network (CAN) bus is attacked during a brake process, the vehicle will lose driving direction control and driver's life will be highly threatened. However, current methods for detecting attacks are not sufficiently accurate and no methods can provide attack mitigation. To ensure vehicle ABS security, we propose an attack detection method to accurately detect both sensor attack and CAN bus attack in a vehicle ABS, and an attack mitigation strategy to mitigate their negative effects on the vehicle ABS. In our attack detection method, we build a vehicle state space equation which considers real-time road friction coefficient to predict vehicle states (i.e., wheel speed and longitudinal brake force) with their previous values. Based on sets of historical measured vehicle states, we develop a search algorithm to find out attack changes (vehicle state changes because of the attack) by minimizing errors between predicted vehicle states and measured vehicle states. In our attack mitigation strategy, attack changes are subtracted from measured vehicle states to generate correct vehicle states for the vehicle ABS. We conducted first real sensor attack experiments to show how a magnet affects sensor readings. Our experimental results demonstrate that our attack detection method can detect sensor attack and CAN bus attack more accurately compared with existing methods, and also our attack mitigation strategy almost eliminates attack's effects on the vehicle ABS.

With ever increasing concerns on environmental issues caused by gasoline fuel based vehicles, electric vehicles (EVs) have attracted more and more attention from governments, industries, and customers [1] . The recent advancements in EVs have great potential to create a more environmentally friendly smart city. However, due to limited battery capacity, most current mainstream EVs still have quite limited driving range (e.g., 100 miles) [2] . How to ensure the continuous running of EVs on a large-scale road network (e.g., metropolitan city, interstate) becomes a major concern.

Electronic control units and actuators in a modern vehicle communicate with each other through controller area network (CAN) bus to ensure driving safety. Current methods assume that time interval or frequency of a normal message always keeps constant and detect abnormal messages by checking whether a message's time interval or frequency changes. However, through real CAN bus dataset analysis, we find that time interval and frequency of a normal message may change greatly in different vehicle driving conditions (such as Acceleration, Deceleration, and Driving mode shift), which can cause low detection accuracy of current methods. To handle this problem, in this paper, we propose a message transmission behavior based abnormal message detection system (MetraDS), which detects abnormal messages in CAN bus based on message transmission behaviors including frequency and message sequence. Through real dataset analysis, we find that a message's sequence usually does not change in different vehicle driving conditions, and also an abnormal message affects the frequency of itself and its subsequent messages. Accordingly, MetraDS checks message sequence dissimilarity to determine whether abnormal messages exist in a message sequence and then checks each message's influence to itself and its subsequent messages to locate the abnormal messages in the sequence. We used the real dataset to test the abnormal message detection performance of MetraDS in comparison with state-of-the-art methods. The experimental results show that MetraDS improves the abnormal message detection accuracy of other methods by as much as 33.8%, and its memory and computational cost requirements are acceptable.

Pure electric vehicles (EVs) are more and more popular in current automotive markets. Many services for EVs such as intelligent battery charging systems and mobile apps are developed to monitor battery energy information (energy consumption time series) for users. However, it is neglected that such EV energy consumption time series may lead to driver privacy related problems such as the disclosure of driving path. In this paper, we propose the first battery energy based path inference attacks (Bepath), which can estimate EV driving paths only based on EV energy consumption time series. Bepath firstly estimates appliance states of an EV including vehicle speed state, air condition state and intersection turn state based on EV energy consumption time series. And then, Bepath figures out the driving path based on the estimated appliance states and map information. We used real-life daily driving data with different participants to test the performance of Bepath and compared inferred path results between Bepath and an existing method to evaluate the path inference accuracy of Bepath. The experimental results show that Bepath can estimate appliance states accurately and infers 50% trips with distance errors within 0.5 km, while the previous work that infers paths based on vehicle speed from insurance companies can only infer 26% trips with distance errors within 0.5 km.

Previous passenger demand inference methods have insufficient accuracy because they fail to catch the influence of all random factors (e.g., weather, holiday). Also, existing taxicab dispatching methods are not directly applicable for electric taxicabs because they cannot optimize their charging. We present CD-Guide: an electric taxicab dispatching and charging approach based on customized training and Reinforcement Learning (RL). We studied a metropolitan-scale taxicab dataset, and found: histogram of passengers' origin buildings (i.e., where they come from) is useful for selecting suitable training data for inference model, passenger demand in different regions may be influenced by various unpredictable random factors, and taxicabs' charging time must be considered to avoid missing potential passengers. By saying suitable historical data, we mean the data that are under the influence of random factors similar as current time. Then, we develop a RL based method to guide a taxicab to maximize its probability of picking up a passenger, minimize the number of its missed passengers due to charging, and meanwhile avoid the taxicab from battery exhaustion. Our trace-driven experiments show that compared with previous methods, CD-Guide increases the total number of served passengers by 100%.

Previous Electric Vehicle (EV) charging scheduling methods and EV route planning methods require EVs to spend extra waiting time and driving burden for a recharge. With the advancement of dynamic wireless charging for EVs, Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to the deployment of MEDs, are not directly applicable for the scheduling of MEDs on city-scale road networks. We present MobiCharger: a Mobile wireless Charger guidance system that determines the number of serving MEDs, and the optimal routes of the MEDs periodically (e.g., every 30 minutes). Through analyzing a metropolitan-scale vehicle mobility dataset, we found that most vehicles have routines, and the temporal change of the number of driving vehicles changes during different time slots, which means the number of MEDs should adaptively change as well. Then, we propose a Reinforcement Learning based method to determine the number and the driving route of serving MEDs. Our experiments driven by the dataset demonstrate that MobiCharger increases the medium state-of-charge and the number of charges of all EVs by 50% and 100%, respectively.

As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. Also, as velocity optimization is for individual vehicles, previous methods cannot avoid rear-end collisions. That is, a vehicle following its optimal velocity profile may experience rear-end collisions with its frontal vehicle on the road. In this article, for the first time, we propose a velocity optimization system that enables EVs to immediately pass green traffic lights without delay and to avoid rear-end collisions to ensure driving safety when EVs follow optimal velocity profiles on the road. We collected real driving data on road sections of US-25 highway (with two driving lanes in each direction and relatively low traffic volume) to conduct extensive trace-driven simulation studies. Results show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time. Also, it helps EVs to avoid possible collisions compared with existing collision avoidance methods.

Accurate estimations on vehicle fuel consumption and pollution emission on roads are important for vehicle velocity optimization and driving route planning. Existing methods for such estimations only consider vehicle driving speed and acceleration but neglect the influence of road gradient. This is mainly because the road gradients for most road networks are not available and none of existing methods for road gradient estimation can be conducted inexpensively in practice and keep high road gradient estimation accuracy simultaneously. Thus, how to estimate the road gradient conveniently and accurately is an important but challenging problem. To handle this challenge, we propose a new road gradient estimation system which estimates the road gradient only using a smartphone. When a vehicle is driving, a smartphone in the vehicle continuously measures vehicle states (velocity, acceleration, steering rate, position), which are used to estimate the road gradient. To eliminate measuring noise and drift noise, the deviation between the measured value and estimated value is used to adjust the estimated value. Since measured vehicle states when a vehicle changes lane adversely influence the accuracy of road gradient estimation, we design lane change detection to eliminate such influences. Finally, given a group of road gradient estimates for a given route, we use the track fusion algorithm to further eliminate measuring noise and drift noise and improve road gradient estimation accuracy. We conducted driving experiments in a city area to evaluate our system. The experimental results show that our system's estimation error is reduced by 22% compared with existing methods. The results also demonstrate the accuracy of our lane change detection. Finally, we integrated the road gradient values into vehicle fuel consumption and air pollution emission model to estimate fuel consumption and air pollution emission and found that the estimation values increase by 33.4% compared with the values without considering road gradient.

As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. In this paper, for the first time, we propose a velocity optimization system which enables EVs to immediately pass green traffic lights without delay. We collected real driving data on a 4.0 km long road section of US-25 highway to conduct extensive trace-driven simulation studies. The experimental results from Matlab and Simulation for Urban MObility (SUMO) traffic simulator show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time.