Uncertainty-Aware Human Intervention for Autonomous Vehicles

Context

The Autonomous Driving market is expected to generate between 300 and 400B by 2035 with studies showing it could reduce the number of accidents by 15% by 2030. In 2021, two-thirds of consumers reported interest in purchasing L4 highway pilots for a one time fee of 10K. However, only 4% of cars are projected to include L3+ AD functions by 2030. Moreover, trust in the technology has declined by 10% with 26% of consumers reporting they would switch to AV in 2021, compared to 35% in 2020. Additionally, perception, prediction, and planning algorithms continue to require significant computational resources and are considered to be the remaining areas of high difficulty according to survey respondents.

Indeed, current AV computations would result in carbon emissions equivalent to that of all existing data centers as shown in a recent MIT study.Despite these challenges, several AV products have begun receiving ISO 26262 certifications, e.g., NVIDIA OS, Mobileye, Apex.ai, leaving computational efficiency, consumer trust, and algorithm robustness as the final barriers for large-scale adoption.

Our Solution

Our research explores how uncertainty-aware systems can identify situations that call for human intervention. The studies below examine navigation and control under specific experimental conditions and provide the technical context for their results.

Additional Resources

Z. Liu, A. Amini, S. Zhu, S. Karaman, S. Han, and D. Rus, “Efficient and Robust LiDAR-Based End-to-End Navigation,” presented at the 2021 IEEE International Conference on Robotics and Automation (ICRA).

A. Amini et al., “VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles,” presented at the 2022 IEEE International Conference on Robotics and Automation (ICRA).

Z. Liu et al., “BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation,” presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA).

A. Amini et al., “Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1143–1150, Apr. 2020.

Amini, Alexander, Wilko Schwarting, Guy Rosman, Brandon Araki, Sertac Karaman, and Daniela Rus. “Variational Autoencoder for End-To-End Control of Autonomous Driving with Novelty Detection and Training De-Biasing,” presented at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).