PNNUAD: Perception Neural Networks Uncertainty Aware Decision-Making for Autonomous Vehicle

Most environment perception methods in autonomous vehicles rely on deep neural networks because of their impressive performance. However, neural networks have black-box characteristics in nature, which may lead to perception uncertainty and untrustworthy autonomous vehicles. Thus, this work proposes a decision-making method to adapt the potential perception uncertainty due to the sensor noises, fuzzy features, and unfamiliar inputs. The whole method is named as Perception Neural Networks Uncertainty Aware Decision-Making (PNNUAD) method. PNNUAD first uses the Monte Carlo dropout method to estimate the perception neural network uncertainty into a distribution around the original output. Then, the perception uncertainty will be considered in a designed reinforcement learning-based planner using a distributed value function. Finally, a backup policy will maintain the vehicle’s performance to avoid disastrous perception uncertainty. The evaluation section uses an augmented reality urban driving scenario; namely, the scenario builds in the CARLA simulator while the perception uncertainty comes from the real dataset. This case study focuses on the object class uncertainty of a widely used neural network, i.e., YOLO-V3. The results indicate that the proposed method can maintain AV safety even with poor perception performance. Meanwhile, the AV has not become too conservative by defending the perception uncertainty. This work is necessary for applying the statistics neural networks to safety-critical autonomous vehicles, and the source code will be open-source in this work.