DeepSafe: High-definition sensor-real edge-case training frameworks for the next generation of robustly trained AVs

自动驾驶汽车（AV）试点研究表明，自动驾驶汽车可以处理“正常”驾驶；99%的日常驾驶体验。然而，事实证明，训练自动驾驶汽车处理道路上可能发生的众多不寻常事件（即所谓的边缘情况）比预期的要困难。当自动驾驶汽车无法理解边缘情况时，驾驶行为就会变得不可靠和不安全，例如过度刹车导致追尾碰撞、车道保持故障，甚至在最坏的情况下发生致命的高速碰撞。自动驾驶汽车必须能够安全可靠地处理边缘情况，才能让公众和消费者接受道路上的自动驾驶汽车，才能让技术开发商和汽车制造商满足即将出台的监管标准，才能让汽车行业从迄今为止在自动驾驶技术上的大量投资中获得回报。针对边缘情况训练自动驾驶感知系统具有挑战性；真实训练数据的数量是有限的。模拟和合成数据被广泛认为是需要的，但目前可用的合成训练数据不足以让模拟训练的人工智能在边缘情况下成功地改善感知和决策。DeepSafe将模拟供应链中的领导者聚集在一起，解决阻碍自动驾驶感知模拟成功训练的综合数据问题。

Autonomous Vehicle (AV) pilot studies have demonstrated that AVs can handle "normal" driving; 99% of our day-to-day driving experience. However it is proving harder than expected to train AVs to deal with the multitudinous unusual events that can happen on the road, known as edge-cases. When an AV fails to understand an edge-case, driving behaviour becomes unreliable and unsafe, with examples include over-braking causing rear-end collisions, lane-keeping failures, and in the worst cases fatal high-speed collisions.It is essential for AVs to be able to handle edge-cases safely and reliably for public and consumer acceptance of AVs on the road, for the technology developers and automotive manufacturers to meet upcoming regulatory standards and so that the automotive industry can realise a return on the considerable investments made to date in AV technology.Training AV perception systems for edge-cases is challenging; the volume of real-life training data is limited. Simulation and synthetic data are widely recognised as being needed but currently available synthetic training data is not sufficiently sensor-real for simulation-trained AI to be successful in improving perception and decision-making on edge cases.DeepSafe brings together leaders in the simulation supply chain to resolve synthetic data issues inhibiting successful training for AV perception using simulation.