L4数据闭环最重要的第一步：选对整个组织的LossFunction

Core Viewpoint - The article emphasizes the importance of defining appropriate primary metrics (loss functions) in autonomous driving data loops, arguing that traditional metrics like MPI (Miles Per Intervention) are inadequate for driving problem-solving and system performance improvement [5][10][87]. Group 1: Data Loop and Metrics - The organization should be viewed as a large model where the primary metric acts as the loss function, guiding the optimization process [15][87]. - The common metric MPI is criticized for focusing on how often human intervention is needed rather than the vehicle's performance in avoiding "stupid" or "dangerous" actions [22][80]. - The article introduces two new metrics: MPS (Miles Per Stupid) and MPD (Miles Per Dangerous), which are more aligned with the actual performance of the autonomous system [10][44][80]. Group 2: Limitations of MPI - MPI is defined as total mileage divided by the number of interventions, which can mislead organizations into optimizing for fewer interventions rather than improving vehicle behavior [18][22]. - The timing of interventions often does not correlate with the actual problems occurring, leading to a misalignment in performance metrics [25][26]. - The article highlights that relying on MPI can create negative incentives, encouraging teams to avoid reporting issues rather than addressing them [26][90]. Group 3: MPS and MPD Implementation - MPS focuses on the frequency of "stupid" actions taken by the vehicle, while MPD addresses "dangerous" actions, providing a clearer picture of system performance [44][80]. - The organization can utilize triggers to define and capture these behaviors, allowing for a more precise analysis of performance [47][85]. - The metrics MPS and MPD can be used to drive self-improvement within the organization, ensuring that the focus remains on enhancing vehicle behavior rather than merely reducing human intervention [87][90]. Group 4: Examples and Case Studies - The article provides examples of how MPS and MPD can be applied in real scenarios, such as analyzing sudden braking events and their causes, which can lead to actionable insights for system improvement [49][51][66]. - It discusses the importance of understanding the context behind performance metrics, emphasizing that both improvements and deteriorations in metrics should be investigated thoroughly [59][78]. - The article concludes that effective metrics should not only reflect performance but also guide the organization towards continuous improvement and problem resolution [87][90].