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Scaled platforms constructed from modified RC cars are popular in the academic and hobby communities. These platforms are typically 0.2 m to 1 m long and weigh between 1 kg and 25 kg. Costs range from a few hundred to tens of thousands of dollars, largely determined by the size, sensors, and computing. Construction, maintenance, and programming is typically handled by a small team of students or researchers. Recently, several open source projects released complete documentation and interface software, which is in contrast to the one-off nature of older work that often lacked enough information to replicate. Documentation for open source platforms normally includes parts lists, build instructions, 3 and interface software for the sensors and actuators. Availability of tutorials, simulation environments, and public datasets vary by project. Common sensors include wheel speed, inertial measurement unit (IMU), cameras, depth sensors, ultrasonic, and light detection and ranging (Lidar) units. The target environment for these platforms is typically indoors on a smooth surface. The Donkey Car [5] is an easy to build 1:16 scale autonomous platform for the DIY Roborace events targeted at hobbyists. Onboard computing and sensing are a Raspberry Pi 3 with a matching wide angle camera. The Berkeley Autonomous Race Car (BARC) [6] is a 1:10 scale vehicle designed as a simple and affordable research platform for self-driving vehicle technologies that has been successfully used to demonstrate various control algorithms. The onboard ODROID-XU4 is similar in computational performance to the Raspberry Pi 3, and the sensor suite includes a hobby grade camera, IMU, four ultrasonic range finders, and Hall effect wheel speed sensors. The F1/10 project [7] and accompanying Autonomous Racing Competition allows teams to race against one another using a common 1:10 scale platform developed at the University of Pennsylvania. Computing on the F1/10 platform is performed by an Nvidia Jetson. The sensor suite includes a hobby IMU, compact indoor Hokuyo 2D Lidar, and optional Structure and Zed depth and motion sensing cameras. The 1:10 scale Rapid Autonomous Complex-Environment Competing Ackermann-steering Robot (RACECAR) [8] from Massachusetts Institute of Technology is a platform for researchers creating applications for self driving cars. RACECAR also uses the Nvidia Jetson for computing, and includes the same Hokuyo Lidar and Zed stereo camera as the F1/10 platform. Table 1 provides a comparison of these open source scaled platforms.

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A distributed brake-by-wire system provides flexible and precise braking force control with shorter or no brake pipes. Fail-safe control is a critical part of the system which guarantees braking safety during failure of the brake actuators. The purpose of this paper is to develop a fail-safe control strategy that cooperates better with the driver so as to enhance the fail-safe control performance. Therefore, the driver’s behaviour should be taken into account during the design of fail-safe control strategies. Two design goals considering the driver’s behaviour are proposed. The first concerns the braking performance; the second concerns whether the driver can handle the failure situation easily. A qualified fail-safe control strategy should meet both the design goals. The driver model used includes an anti-wind-up proportional–integral controller braking operation model and an optimal preview control steering operation model. Two different fail-safe control strategies, namely strategy I and strategy II, are proposed and examined with the design goals. Strategy I is designed to follow the nominal motions of the vehicle in all the three degrees of freedom in the yaw plane. The pseudo-control vector is determined by sliding-mode controllers and allocated to follow the nominal motions. Strategy II is designed to minimize the braking force imbalance between the left side and the right side of the vehicle on the premise that the brake deceleration demand has been achieved. Simulation results show that strategy I generates a longer braking distance and requires a harder brake pedal force, which violates both the design goals. Strategy II meets both the design goals and therefore is a qualified strategy to cooperate with the driver model. Its capability to be implemented in real time is verified on a hardware-in-the-loop test bench. Its advantages considering the unmodelled behaviour of the driver is also discussed. The distributed electrohydraulic braking system developed is used as the distributed brake-by-wire system.

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