Adaptive Optimization of Autonomous Vehicle Computational Resources for Performance and Energy Improvement
Autonomous vehicles usually consume a large amount of computational power for their operations, especially for the tasks of sensing and perception with artificial intelligence algorithms. Such a computation may not only cost a significant amount of energy but also cause performance issues when the onboard computational resources are limited. To address this issue, this paper proposes an adaptive optimization method to online allocate the onboard computational resources of an autonomous vehicle amongst multiple vehicular subsystems depending on the contexts of the situations that the vehicle is facing. Different autonomous driving scenarios were designed to validate the proposed approach and the results showed that it could help improve the overall performance and energy consumption of autonomous vehicles compared to existing computational arrangement.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingAutonomous VehiclesSimilar Papers 제목 키워드 기반
Bandwidth-adaptive Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehicles
A key challenge for autonomous driving lies in maintaining real-time situational awareness regarding surrounding obstacles under strict latency constraints. The high processing requirements coupled with limited onboard c…
3D Object DetectionAutonomous VehiclesAutonomous DrivingLarge-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
Edge intelligence autonomous driving (EIAD) offers computing resources in autonomous vehicles for training deep neural networks. However, wireless channels between the edge server and the autonomous vehicles are time-var…
Autonomous DrivingAutonomous VehiclesAdaptive Evolutionary Framework for Safe, Efficient, and Cooperative Autonomous Vehicle Interactions
Modern transportation systems face significant challenges in ensuring road safety, given serious injuries caused by road accidents. The rapid growth of autonomous vehicles (AVs) has prompted new traffic designs that aim …
Autonomous VehiclesLearning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles
The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversarial image perturbations. In the existing…
Autonomous Vehiclesobject-detectionObject DetectionStochastic OptimizationVariations in Multi-Agent Actor-Critic Frameworks for Joint Optimizations in UAV Swarm Networks: Recent Evolution, Challenges, and Directions
Autonomous unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can effectively execute surveillance, connectivity, and computing services to ground users (GUs). These missions require trajectory planning, UAV-GUs assoc…
Deep Reinforcement LearningTrajectory Planning