The designed core provides a 200% increase in peak throughput per PE count while only incurring a 6% increase in area overhead compared to a single, linear multiplier PE core with same output bit precision.
This paper presents TrolleyMod v1. 0, an open-source platform based on the CARLA simulator for the collection of ethical decision-making data for autonomous vehicles.
Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of this paradigm.
Recent developments have established the vulnerability of deep reinforcement learning to policy manipulation attacks via intentionally perturbed inputs, known as adversarial examples.
With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments.
The complexity of dynamics in AI techniques is already approaching that of complex adaptive systems, thus curtailing the feasibility of formal controllability and reachability analysis in the context of AI safety.
Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations.
In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases.