ﻻ يوجد ملخص باللغة العربية
Autonomous vehicles bring the promise of enhancing the consumer experience in terms of comfort and convenience and, in particular, the safety of the autonomous vehicle. Safety functions in autonomous vehicles such as Automatic Emergency Braking and Lane Centering Assist rely on computation, information sharing, and the timely actuation of the safety functions. One opportunity to achieve robust autonomous vehicle safety is by enhancing the robustness of in-vehicle networking architectures that support built-in resiliency mechanisms. Software Defined Networking (SDN) is an advanced networking paradigm that allows fine-grained manipulation of routing tables and routing engines and the implementation of complex features such as failover, which is a mechanism of protecting in-vehicle networks from failure, and in which a standby link automatically takes over once the main link fails. In this paper, we leverage SDN network programmability features to enable resiliency in the autonomous vehicle realm. We demonstrate that a Software Defined In-Vehicle Networking (SDIVN) does not add overhead compared to Legacy In-Vehicle Networks (LIVNs) under non-failure conditions and we highlight its superiority in the case of a link failure and its timely delivery of messages. We verify the proposed architectures benefits using a simulation environment that we have developed and we validate our design choices through testing and simulations
Autonomous vehicles face tremendous challenges while interacting with human drivers in different kinds of scenarios. Developing control methods with safety guarantees while performing interactions with uncertainty is an ongoing research goal. In this
Widespread adoption of autonomous cars will require greater confidence in their safety than is currently possible. Certified control is a new safety architecture whose goal is two-fold: to achieve a very high level of safety, and to provide a framewo
Motion prediction of vehicles is critical but challenging due to the uncertainties in complex environments and the limited visibility caused by occlusions and limited sensor ranges. In this paper, we study a new task, safety-aware motion prediction w
Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous streams of heavy-duty mission-critical processing tasks, such as real-time obstacle detection and navigation. However, in practical applications, erratic patterns in channe
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training)