自动驾驶中动态截止时间的节能Adaptive System重构
Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving
摘要：
自动驾驶应用日益增长的计算需求使得能源优化对于降低电池容量和车辆重量至关重要。当前的能量最优化方法通常以具有静态期限的传统实时系统为目标，导致保守的能量节省，由于车辆的速度和驾驶环境的变化引起的动态期限，所述保守的能量节省不能利用额外的能量优化。我们提出了一种自适应系统优化和重新配置的方法，动态适应调度参数和处理器的速度，以满足动态的最后期限，同时消耗尽可能少的能量。我们对博世自动驾驶任务集和真实驾驶数据的实验结果显示，与传统静态能量最优化方法相比，在典型动态驾驶场景下，平均能耗降低高达46.4%，这表明通过利用动态截止时间来实现动态能量优化的巨大潜力。
英文摘要：
The increasing computing demands of autonomous driving applications make energy optimizations critical for reducing battery capacity and vehicle weight. Current energy optimization methods typically target traditional real-time systems with static deadlines, resulting in conservative energy savings that are unable to exploit additional energy optimizations due to dynamic deadlines arising from the vehicle's change in velocity and driving context. We present an adaptive system optimization and reconfiguration approach that dynamically adapts the scheduling parameters and processor speeds to satisfy dynamic deadlines while consuming as little energy as possible. Our experimental results with an autonomous driving task set from Bosch and real-world driving data show energy reductions up to 46.4% on average in typical dynamic driving scenarios compared with traditional static energy optimization methods, demonstrating great potential for dynamic energy optimization gains by exploiting dynamic deadlines.
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