  A deep reinforcement learning based multi-objective autonomous braking system
is presented. The design of the system is formulated in a continuous action
space and seeks to maximize both pedestrian safety and perception as well as
passenger comfort. The vehicle agent is trained against a large naturalistic
dataset containing pedestrian road-crossing trials in which respondents walked
across a road under various traffic conditions within an interactive virtual
reality environment. The policy for brake control is learned through computer
simulation using two reinforcement learning methods i.e. Proximal Policy
Optimization and Deep Deterministic Policy Gradient and the efficiency of each
are compared. Results show that the system is able to reduce the negative
influence on passenger comfort by half while maintaining safe braking
operation.
