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Vehicle Controllers and Communication
Published in Iqbal Husain, Electric and Hybrid Vehicles, 2021
Class D networks include the fastest communication protocol in the automobiles. This class targets future protocols for in-vehicle communications as well. Media-oriented systems transport (MOST) and Flexray are example protocols for Class D, which are making their way into modern automobiles. MOST is the vehicle communication bus standard intended for interconnecting multimedia components. A reliable, high-speed communication network is also essential for safety critical components for today’s automobiles, particularly as drive-by-wire systems get introduced in a vehicle. Drive-by-wire technologies include steer-by-wire, brake-by-wire, throttle-by-wire, etc.; these technologies will replace all the existing hydraulic and mechanical systems currently in place. In addition, the control command for all of the electric powertrain in hybrid and electric vehicles are by-wire technologies. The safety-critical and propulsion components require reliable and deterministic time triggered protocols instead of event triggered protocol such as that in the CAN. The Flexray protocol, which is being developed by the Flexray consortium, provides high-speed, deterministic, fault-tolerant message transmission essential in drive-by-wire and electric powertrain components.
Hardware
Published in Hanky Sjafrie, Introduction to Self-Driving Vehicle Technology, 2019
Drive-by-wire systems have been a key enabler for innovative ADAS applications such as adaptive cruise control and lane assist systems. At the same time, however, they also pose a significant risk of unauthorized vehicle control by paving the way for tampering and the injection of fake messages for the ECUs. Modern vehicles generally come with an extra layer of security to make it more difficult for hackers to manipulate the drive-by-wire system. Older vehicles are more vulnerable, however, and someone who knows how to interpret and manipulate the internal bus messages for the drive-by-wire system may be able to gain control of the vehicle.
Architecture of an Automotive Power System
Published in Dorin O. Neacşu, Automotive Power Systems, 2020
A modern concept applied to the architecture of electrical distribution system involves drive-by-wire (DbW) technology, which uses a central computer and various electrical actuators for the linkage of power. The main controller is similar to a video game controller in complexity and internal architecture. The driver can benefit from a user interface to fine-tune vehicle handling without changing anything in the car’s mechanical components. Examples of sub-systems that use the drive-by-wire concept may include electronic throttle control, brake-by-wire, power steering, so on.
Human-centred design of next generation transportation infrastructure with connected and automated vehicles: a system-of-systems perspective
Published in Theoretical Issues in Ergonomics Science, 2023
Yiheng Feng, Yunfeng Chen, Jiansong Zhang, Chi Tian, Ran Ren, Tianfang Han, Robert W. Proctor
The information-processing model of CAVs is like that for humans, which includes sensing, perception, decision making (or path planning in CAV) and control. A range of sensors including camera, radar, and Lidar are essential for CAVs to sense and perceive the environment. CAVs need to identify static objects such as traffic lights, lane markings, and road barriers, as well as dynamic objects such as vehicles and pedestrians, and their relative locations and speeds (Rosique et al. 2019). After their construction of the driving environment and localization of the vehicles, CAVs plan paths in the short term (i.e. 5–10 s) with different objective functions such as safety, efficiency, and driver’s (rider’s) comfort (Dolgov et al. 2010). Finally, the planned path is executed to control the CAVs’ maneuvers through the throttle, brake, and steering wheel. Typically, drive-by-wire control technology is applied, which replaces traditional mechanical-based control with electronic signals (Isermann, Schwarz, and Stolzl 2002). Machine Learning (ML) techniques are widely adopted in the information-processing pipeline. They can be applied in each component of the autonomy stack, or the entire system can be treated as a ‘black box’ to implement the End2End learning approach, in which sensed input is mapped directly to vehicle steering commands (Grigorescu et al. 2020).