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Environmental perception is a crucial subsystem in autonomous vehicles. In order to build safe and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than two sensors) should balance the degree of alignment between each two sensors.

With the increasing worldwide concern on environmental pollution, battery electrical vehicles (BEV) have attracted a lot attention. However, it still couldn’t satisfy the market requirements because of the low battery power density, high cost and long charging time. The range-extended electrical vehicle (REEV) got more attention because it could avoid the mileage anxiety of the BEVs with lower cost and potentially higher efficiency. When internal combustion engine (ICE) works as the power source of range extender (RE) for REEV, its NVH, emissions in starting process need to be optimized. In this paper, a 2-cylinder PFI gasoline engine and a permanent magnet synchronous motor (PMSM) are coaxially connected. Meanwhile, batteries and load systems were equipped. The RE co-control system was developed based on Compact RIO (Compact Reconfigurable IO), Labview and motor control unit (MCU).

Proton exchange membrane fuel cell (PEMFC) system is expected to be the next generation vehicle powertrain. However, water management is still the main problem which directly affects the performance, reliability and durability in PEMFC system. To ensure the accurate water content estimation, High Frequency Resistance (HFR) is the most representative indicator in laboratory. The HFR is calculated by detecting the Alternating Current (AC) voltage response of fuel cell under the excitation of 1k Hz AC current. The voltage level of the AC excitation affects the measurement of HFR. Generally, 5mV AC excitation is used to measure HFR for the fuel cell with an output voltage between 0.6-1V. So, online HFR monitor is a big challenge for vehicle application, due to its low Signal/Noise Ratio (S/N), poor Electromagnetic Compatibility (EMC) environment, high common mode voltage and several hundred cells. In this work, an online fuel cell HFR monitor controller is developed.

The radar-based advanced driver assistance systems (ADAS) like autonomous emergency braking (AEB) and forward collision warning (FCW) can reduce accidents, so as to make vehicles, drivers and pedestrians safer. For active safety, automotive millimeter-wave radar is an indispensable role in the automotive environmental sensing system since it can work effectively regardless of the bad weather while the camera fails. One crucial task of the automotive radar is to detect and distinguish some objects close to each other precisely with the increasingly complex of the road condition. Nowadays almost all the automotive radar products work in bidimensional area where just the range and azimuth can be measured. However, sometimes in their field of view it is not easy for them to differentiate some objects, like the car, the manhole covers and the guide board, when they align with each other in vertical direction.

There are many electronic devices in electric vehicle (EV), making its electromagnetic compatibility (EMC) serious. Motor drive system is the main interference source of EV, whose electromagnetic interference (EMI) is much worse than conventional vehicle. In this paper, the motor drive system of EV was mainly researched, and a co-simulation method was proposed: control system and motor model were established with Matlab, and the equivalent circuit model of inverter and the cable model were established with Saber. By this way, a complete motor drive system model for conductive EMI was obtained. This modeling method can not only accurately establish the EMI sources and coupling paths, but can simulate the control strategy and operating conditions.

As the connectivity of vehicles increases rapidly, more vehicles have the capability to communicate with each other. Because Vehicular Ad-hoc NETworks (VANETs) have the characteristics of solid mobility and decentralization, traditional security strategies such as authentication, firewall, and access control are difficult to play an influential role. As a soft security method, trust management can ensure the security attributes of VANETs. However, the rapid growth of newly encountered nodes of the trust management system also increases the requirements for trust establishing mechanisms. Without a proper trust establishment mechanism, the trust value of the newly encountered nodes will deviate significantly from its actual performance, and the trust management system will suffer from newcomer attacks.

Automotive embedded DCS is widely used to solve automotive control problems. This paper presents a model-driven development technology for such systems. Models of automotive embedded DCS are built up strictly complying with the four-layer-model architecture, which is presented by Model-Driven Architecture (MDA). Three kinds of models are used to describe the protocol data structure, the algorithm process and visualization aspects of automotive embedded DCS. Corresponding XML databases are created based upon these models. As a single data source, these databases play key roles in further development phases, including generating the protocol specification, MC&D systems and embedded programming, etc. Some demonstrative applications are presented in this paper.

For high levels autonomous driving functions, the Decision Layer often takes on more responsibility due to the requirement of facing more diverse and even rare conditions. It is very difficult to accurately find a safe and efficient lane change timing when autonomous vehicles encounter complex traffic flow and need to change lanes. The traditional method based on rules and experiences has the limitation that it is difficult to be taken into account all possible conditions. Therefore, this paper designs a lane-changing decision algorithm based on data-driven and machine learning, and uses the DQN (Deep Q Network) algorithm in Reinforcement Learning to determine the appropriate lane-changing timing and target lane. Firstly, the scene characteristics of the highway are analyzed, the input and output of the decision-making model are designated and the data from the Perception Layer are processed.

Intrusion Detection Systems (IDS), technically speaking, is to monitor the network, system, and operation status according to certain security policies, and try to find various attack attempts, attacks or attack results to ensure the confidentiality, integrity and availability of network system resources. Automotive intrusion detection systems can identify and alert by analyzing in-vehicle traffic and log when software applications or devices with malicious activity exist, or the in-vehicle network is tampered and injected. But unfortunately, automotive cybersecurity researchers hardly produce a comprehensive detection method due to the confidential nature of Controller Area Network (CAN) DBC format files, which is a standard long maintained by car manufacturers. In this paper, an enhanced intrusion detection method is proposed based on the double-decision-tree to classify different attack models for in-vehicle CAN network without the need to obtain complete DBC files.

Electromagnetic compatibility of electric vehicles is withstanding great challenges because of multiple ECU and actuators distributed in vehicle. At present, researches on electromagnetic characteristics of vehicle mainly focus on rectification based on the EMC experiment, which take much time and energy. Thus, this paper adopts the way of computer simulation studying the electromagnetism model and electromagnetic characteristics of electric vehicle with following procedure. Firstly, the equivalent model of wiring harness was deduced and built. Harnesses were divided into different groups according to terminal reflection property. The equivalent unit parameter matrix was calculated to build the equivalent wiring harness. The crosstalk and radiation cases were set to check the equivalent harness method.

Lane Keeping Assistance (LKA) system is a very important part in Advanced Driver Assistance Systems (ADAS). It prevents a vehicle from departing out of the lane by exerting intervention. But an inappropriate performance during LKA intervention makes driver feel uncomfortable. The intervention of LKA can be divided into 3 parts: intervention timing, intervention process and intervention ending. Many researches have studied about the intervention timing and ending, but factors during intervention process also affect driver feelings a lot, such as yaw rate and steering wheel velocity. To increase driver’s acceptance of LKA, objective and subjective tests were designed and conducted to explore important indices which are highly correlated with the driver feelings. Different kinds of LKA controller control intervention process in different ways. Therefore, it’s very important to describe the intervention process uniformly and objectively.

Smart electric vehicles need more accurate and more timely information as well as control than traditional vehicles, which depends on great environmental sensors such as millimeter-wave radar. In this way, the electromagnetic compatibility of whole vehicle would confront more serious challenges because of its high frequency range. Thus, this paper studies the electromagnetic distribution and electromagnetic interference suppression of smart electric vehicles with the followings. Firstly, the millimeter wave radar is modeled and optimized. Micro strip patch antenna, with small size, light mass and low cost, is used as array element of antenna. Millimeter wave radar is modeled and simulated step by step from array element to line array to planar matrix. Then the Cross Shape - Uniplanar Compact - Electromagnetic Band Gap (CS-UC-EBG) structure is deployed to optimize its electromagnetic characteristics, based on finite time domain difference model theory.

Nowadays researches of automotive electromagnetic field mainly focus on the component level and electromagnetic compatibility, while there is a lack of relevant studies on internal electromagnetic environment of the vehicles. With the increasingly complex internal electromagnetic environment of the vehicle, people are increasingly concerned about its potential impact of human health. This article researches on a type of electric vehicle and the occupants and analyses its electromagnetic radiation effects on human health. Firstly, considering the characters of Pro/E, Hypermesh and FEKO, the “Characteristics grouping subdivision” method is used to establish the entire vehicle body FE model. According to the requirement of MOM/FEM method, the entire vehicle model is optimized to be a high quality body model with simple construction and moderate grid size.

From the perspective of the three elements of electromagnetic interference, the main function of shielding is to cut off the propagation path of electromagnetic noise. The battery pack casing can be regarded as shielding the electromagnetic interference conducted on its internal and external wiring harnesses, but because the battery pack casing has power lines in and out, the battery pack casing is an incomplete shield. In the field of electromagnetics, shielding can be divided into electrical shielding, magnetic shielding and electromagnetic shielding. Therefore, this paper studies its influence on the electromagnetic radiation emission of the whole vehicle from the perspective of shielding mechanism. Due to the role of the switch components in the power battery system, strong current fluctuation di/dt and voltage fluctuation dv/dt will be generated on the power cable, and these interferences will have an important impact on the radiation emission of the vehicle.

Object detection is an important visual content of the autonomous vehicle, the traditional detecting methods usually cost a lot of computational memory and elapsed time. This paper proposes to use lightweight deep convolutional neural network (MobilenetV3-SSDLite) to carry out the object detection task of autonomous vehicles. Simulation analysis based on this method is implemented, the feature layer obtained after h-swish activation function in the first Conv of the 13th bottleneck module in MobilenetV3 is taken as the first effective feature layer, and the feature layer before pooling and convolution of the antepenultimate layer in MobilenetV3 is taken as the second effective feature layer, and these two feature layers are extracted from the MobilenetV3 network.

Activation loss, mass transfer loss and ohmic loss are the three main voltage losses of the polymer electrolyte membrane fuel cell. While the former two types are relevant to concentration of oxygen in catalyst layer and the later one is associated with the water content in membrane. Distributions of water content and oxygen in a single cell are inconsistent which cause that current densities in each segment of the single cell are different. For the dry inlet gas, the water in the segments near the gas inlet channel will be carried to the segments near the gas outlet channel, which causes high ohmic loss of the segments near the gas inlet channel. In this work, a transfer non-isothermal quasi-three-dimensional model is developed to investigate inconsistency of current densities.

Automotive security has become one of important topics in recent years under new automotive Electronic and Electrical Architecture (EEA). With the development of Intelligent Connected Vehicle (ICV), it has become possible to hack an automotive through in-vehicle networks. The introduction of Information Communications Technology (ICT) brings more risk threats to automotive. Researchers have shown that an attacker can easily tamper with many automotive functions via On-Board Diagnostic II (OBD-II) or In-Vehicle Infotainment (IVI). In order to protect automotive against malicious attacks, automotive security risks were analyzed and then security mechanisms based on network firewall were designed in this paper. Automotive network firewall is a security system that monitors and controls incoming and outgoing network traffics of automotive based on predetermined security rules. The main functions of network firewall include packet filter, anti-DoS and access control.

With the explosion of in-vehicle data, Time Sensitive Network (TSN) is increasingly becoming the backbone of the in-vehicle network to ensure deterministic real-time communication and Quality of Service (QoS). However, legacy buses such as CAN FD and LIN will not disappear for a long time in the future. Many protocols are deployed in the gateway and it is an important component in the security and functional safety of the communication process. In this paper, the recommended Electrical/Electronic Architecture is first given and the use cases for the TSN/CAN FD gateway are illustrated. Then, a TSN/CAN FD routing mechanism is designed and security mechanisms are deployed. The routing mechanism includes the protocol conversion module, queue cache module, and forwarding scheduling module. The protocol conversion module unpacks or packs the TSN or CAN FD frames according to the routing table.

Permanent magnet synchronous motors (PMSM) are widely used in the electric vehicles for their high power density and high energy efficiency. And the motor control system for electric vehicles is one of the most critical safety related systems in electric vehicles, because potential failures of this system can lead to serious harm to humans’ body, so normally a high automotive safety integrity level (ASIL) will be assigned to this system. In this paper, an ASIL-C motor control system based on a multicore microcontroller is presented. At the same time, due to the increasing number of connectivity on the vehicle, secure onboard communication conformed to the AUTOSAR standard is also implemented in the system to prevent external attacks.

In the automotive network architecture, the basic functions of gateway include routing, diagnostic, network management and so on. With the rapid development of connected vehicles, the cybersecurity has become an important topic in the automotive network. A spoof ECU can be used to hack the automotive network. In order to prevent the in-vehicle networks from attacking, the automotive gateway is an important part of the security architecture. A secure gateway should be able to authenticate the connected ECU and control the access to the critical network domain. The data and signals transferred between gateway and ECUs should be protected to against wiretap attacking. The purpose of this paper is to design a secure gateway for in-vehicle networks. In this paper, the designing process of the automotive secure gateway is presented. Based on the threat analysis, security requirements for automotive gateway are defined.