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

自动驾驶汽车(AV)的试点研究表明，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可能在道路上发生的大量不寻常事件，即所谓的边缘情况，比预期的要困难。当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、车道保持故障，在最糟糕的情况下还会发生致命的高速碰撞。对于汽车来说，能够安全可靠地处理边缘情况是至关重要的，这样公众和消费者才能在道路上接受汽车，技术开发人员和汽车制造商才能满足即将到来的监管标准，这样汽车行业才能从迄今在无人机技术上进行的可观投资中获得回报。针对边缘案例培训无人机感知系统是具有挑战性的；现实生活中培训的数据量是有限的。模拟和合成数据被广泛认为是必要的，但目前可用的合成训练数据对传感器的真实性不足以使模拟训练的人工智能成功地改善边缘情况下的感知和决策。DeepSafe将模拟供应链中的领导者聚集在一起，以解决阻碍使用模拟进行AV感知的成功训练的合成数据问题。

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.