The progression of vehicle active safety research and its implementation have brought a lot of reduction in the number of road fatalities globally. It enables the vehicle to aid the driver in the risky scenarios. One of the examples is Autonomous Emergency Braking System (AEB). It yields the required braking torques intervention in the hazardous events to mitigate collision risks. However, statistics show that a large number of road accidents remain, including the case of near-miss incidents. In this work, to aid the reduction of the near-miss accident, and assist the AEB to maintain the safe-distance with the frontal obstacle, AEB is integrated with the Potential Field (PF) risk assessment strategy. When the PF threshold of the frontal obstacle's risk is violated, AEB provides the active braking intervention. The proposed design was tested and validated using a test vehicle, where it mitigates the collision risk with a static obstacle in a medium-speed scenario. The work is part of the continuous effort to develop the autonomous vehicle and reduce the high number of road accidents in Malaysia. Results show the integration of AEB and PF successfully aid the vehicle to mitigate the collision as well as maintaining the safe distance with the frontal obstacle.

The growth of automation sector has brought numerous autonomous technology developments in many sectors, especially automotive. The autonomous features are proven to be helpful in reducing road fatalities globally. Advanced Driver Assistance Systems (ADAS), a system which helps the driving process automation, has growing roles in recent road vehicle features. It is the base for the development of a fully autonomous vehicle. One of the main features of ADAS is Collision Avoidance (CA) system. A sufficient CA architecture usually encompasses threat assessment, path planning and path tracking strategies. There are many ways of developing precise CA architecture using the combination of these strategies. This paper aims to review current available methods for CA as an introductory idea for researchers who are new to this field. Each of the methods in each strategy is categorised into several groups. Their advantages and drawbacks are discussed. In addition to that, several improvement suggestions for a comprehensive CA system were highlighted.

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.