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.

This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result.

This paper presents experimental study on exploring Laser Odometry based LOAM for urban area applications. Most Odometry techniques to determine trajectories of vehicle in urban area use GPS, IMU, or Camera as sensing element. These sensors have their own weakness such as GPS prone to signal lost; IMU suffers from high drift error; and camera dependency to lighting conditions. Meanwhile, LOAM uses 3D LiDAR sensor and possess several advantages such as robust Odometry calculations and more resilient to lighting condition. LOAM was formulated using feature based detection to recognize feature point from edge line and planar surface inside the environment. These features are then used as reference points. The vehicle's position was estimated using dead reckoning method by comparing surrounding reference points with previously determined position. Performance evaluations are performed by comparing the recorded vehicle trajectory data against Google Cartographer which have centimetres accuracy. The result show that the approached strategy has comparable performance with Cartographer in urban area environment. It can achieve drift error between 0.6 and 10.0 metres during short distance travel (<; 1.5 Km).