A 10,000-word article on automobile control in the intelligent era

Fig.5
Connectivity for improved fuel economy and reduced emission

In urban expressways and highways, due to the relatively simple road traffic conditions, the energy-saving potential is mainly obtained by considering information such as road slope and curvature and vehicle speed perturbations, such as using the road slope and GPS positioning to comprehensively optimize the completion time and energy consumed of driving tasks [154−155]. Compared with highways, urban road conditions are more complex and have more constraints and scenarios. Therefore, currently, intelligent energy saving under urban road conditions is mainly concentrated in certain specific scenarios. Considering future traffic light information and predicted speed information, planning the driving speed can effectively reduce the waiting time of vehicles at red lights. The goal of energy saving can be achieved while optimizing the driving speed of electric vehicles [130,156−157]. For the speed planning problem of electric vehicles on fixed roads, the motor and battery efficiency factors are usually considered, and the motor torque, braking force and energy recovery during the driving process of electric vehicles are optimized to achieve an increase in the vehicle's cruising range [158−162]. Under actual road driving conditions, the driving behavior of the vehicle in front has a great impact on the energy consumption of the vehicle. Therefore, under the framework of the predictive cruise control system, appropriately relaxing the cruising speed and tracking requirements for the distance between the front and rear vehicles can improve the vehicle's energy-saving potential [156,163].

At present, research on intelligent energy-saving technology is mainly aimed at optimizing the energy efficiency of power sources, and little attention is paid to the impact of auxiliary equipment (such as air conditioning systems and lighting systems) on the energy consumption of the entire vehicle. In fact, the energy consumption of the air conditioning system is quite considerable, which has a great impact on the mileage of the entire vehicle. The higher the degree of electrification of the vehicle, the higher the proportion of energy consumption of the air conditioning system in the overall energy consumption. A statistic from the United States shows that the amount of fuel consumed by air conditioning in light passenger vehicles in the United States can reach 7 billion gallons per year[164]. The Argonne National Laboratory in the United States conducted a summer vehicle test on the Ford Focus pure electric vehicle. Under the UDDS urban working condition, the vehicle's mileage will be shortened by 53.7 due to the influence of air conditioning refrigeration. %[165]. In view of winter urban conditions, the Argonne National Laboratory in the United States also conducted a real vehicle test on the 2010 Toyota Prius hybrid vehicle and analyzed the impact of the heater on the fuel consumption of the whole vehicle[166]. Therefore, expanding thermal management into a control dimension and studying the integrated optimization control of energy and heat of the vehicle by adjusting the output of the air conditioner can further tap the energy-saving potential of the vehicle[167]. In addition, the current intelligent energy-saving optimization scheme is mainly focused on a specific scenario. On the one hand, the energy-saving effect of the vehicle system in a complex and changeable traffic environment is difficult to evaluate. On the other hand, there is little research on energy-saving control under multi-source information fusion. Therefore, the integration method of vehicle intelligent energy-saving technology with intelligent transportation and road information needs to be further improved. In the process of intelligent energy-saving research, the single consideration of vehicle energy efficiency improvement often brings other performance degradation problems such as "energy-saving discomfort", "insufficient energy-saving power", "battery aging too fast", etc. Multi-objective intelligent energy-saving methods that consider battery health management, driving comfort and safety are also future research hotspots.

3.4.3 Intelligent Emission Reduction of Connected Vehicles

In a congested urban traffic environment, engines frequently operate in transient conditions, resulting in relatively poor emissions. On the one hand, post-processing technology has been making continuous progress [15]. On the other hand, similar to intelligent energy-saving technology, engine emission control combined with network information also provides an opportunity to improve the engine's transient original emissions.

Generally speaking, in an intelligent network environment, based on V2V information, feedback control can be used to ensure smooth driving of vehicles, which can reduce vehicle pollution emissions and fuel consumption during the start/stop process [168]. For traffic scenarios with multiple intersections, cooperative vehicle intersection control can not only reduce rear-end collisions, but also reduce vehicle emissions [169]. Furthermore, the coordinated control of traffic light timing and vehicle driving trajectory is of great significance to emission optimization [170]. For example, the communication between vehicles and dynamic traffic lights can be combined to optimize vehicle speed, gear position, etc. to achieve CO, NOx As well as the reduction of particulate matter and other emissions [171−174]. At present, some achievements in emission reduction based on intelligent network information mainly optimize the cruising speed with the goal of minimizing fuel consumption or reducing congestion time to achieve the purpose of energy conservation and emission reduction. In the future, with the increasingly stringent emission regulations, predictive emission reduction control of heavy-duty diesel commercial vehicles and integrated coordinated control of fuel consumption and emissions based on network information will be the research trend of heavy-duty vehicles in the future. In addition, multi-vehicle coordination and multi-vehicle platoon control based on network information are also hot topics in the research of intelligent vehicle energy conservation and emission reduction [175−176].

3.4.4 Predictive Safety Control of Connected Vehicles

Predictive safety control combined with network information provides an opportunity to improve the active safety of automobiles. The concept of predictive safety control mentioned here is shown in Figure 6. Predictive safety control technology uses traffic preview information obtained from network information such as V2V and V2I, and uses the vehicle dynamics model to predict the future dynamics information of the vehicle, and then applies the model predictive control method to rolling optimize the safety performance indicators, guiding the vehicle to make predictive control decisions on steering, braking, driving and other actuators before encountering dangerous hidden dangers, so as to avoid dangers to the greatest extent and reduce the occurrence of traffic accidents. Typical examples of predictive safety control include automatic Active obstacle avoidance system for the main driving vehicle [177,180], stability control after tire blowout [179], lane keeping [178], and path planning and tracking control for autonomous driving vehicles [181]. Considering multiple variables and constraints, the coordinated control of torque distribution and steering in four-wheel drive vehicles [182−183] to ensure yaw rate tracking and vehicle stability remains a challenging problem. For vertical active/semi-active suspension control, using cloud data or camera information, potholes ahead can be predicted in advance, and the comfort and safety of the vehicle can be coordinated by adjusting the suspension [184].

In addition, due to improper operation of the driver or bad road conditions, the vehicle often enters the extreme conditions in the nonlinear area, seriously endangering the driver's life. Therefore, the coordinated control of vehicle dynamics under extreme conditions is the key technology and development trend in the future. The core problem of active safety control technology under extreme conditions is how to formulate the control system architecture with the highest controllability of active safety functions, and give the mathematical expression of conflicting optimization goals according to the needs of lateral-longitudinal-vertical active safety functions under the current working conditions, and then use traffic preview information and dynamic prediction models to directly optimize the control actions of the underlying actuators to achieve active safety control of critical stability conditions. When the system is in an unstable state, the optimization results of the safe driving area are used to plan a feasible safe driving trajectory, and then by tracking and controlling the unstable equilibrium point in the unstable state, drift control or secondary collision avoidance control can be achieved to improve the active safety control performance of the vehicle.

3.5 Intelligent Vehicle Control with Human in the Loop

In the process of automobile intelligence and automation from low to high, cars will have to "deal" with people for a long time in the future. Human in the loop involves issues such as human-machine interaction takeover during the autonomous driving of a single vehicle from a micro perspective, and from a macro perspective, it involves issues such as the coordination of autonomous vehicles and human-driven vehicles under different autonomous driving vehicle penetration rates.

3.5.1 Human-machine co-driving under hybrid augmented intelligence

Fig.6
Schematics of predictive safety control concept

Although artificial intelligence has penetrated into all aspects of information fusion and perception, decision-making and control of intelligent vehicles, it is difficult to achieve truly full-condition autonomous driving in the short term. Human-machine co-driving, in which the driver and the intelligent system share vehicle control and collaborate to complete driving tasks, will exist for a long time. Therefore, the hybrid enhancement technology of human-machine intelligence that combines the respective advantages of human intelligence and machine intelligence can further promote the development of automotive intelligence and is the future development trend. The so-called hybrid enhanced intelligence is to form a "1+1>2" human-machine cooperative hybrid intelligent system through the hybrid enhancement of human-machine intelligence to form two-way information exchange and control. Human-machine co-driving mainly involves the research of driver intention/behavior modeling and human-machine collaboration: driver intention recognition mainly relies on the driver's actions and postures, vehicle status and traffic environment to infer and estimate the driver's intentions [185], while driving behavior modeling involves simulating the characteristics of preview tracking during human driving [186] and human-vehicle- The changing characteristics of the closed-loop road system as it migrates over time and space[187]; currently, human-machine co-driving can be divided into intelligent driving assistance that increases the driver's perception ability[188], the switching of human-machine driving rights in specific scenarios[189−191], and dynamic allocation of driving rights under human-machine collaboration[192−193].