A Novel Scoring Method for Pedestrian Automatic Emergency Braking Systems Recently, the research and development of advanced driver assistance systems (ADAS) have been studied extensively. Many such features, including lanekeeping assist (LKA), automatic emergency braking (AEB) systems, are made available in commercial vehicles. Due to their complex nature and different design principles, the performance of different invehicle ADAS systems varies significantly. In this paper, we proposed a novel scoring method for AEB systems for pedestrians, which considers and integrates three key variables: 1) Percentage of kinetic energy reduction; 2) Deceleration margin, and 3) Driving smoothness. This method takes the scoring schemes of onroad testing as a reference, which provides a more comprehensive method when assessing the system performance. Field test data is also collected and analyzed to show the calculation procedure and the effectiveness of the proposed approach.

Pedestrian Collision Avoidance System for Scenarios with Occlusions Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.