Abstract
Despite the potential of autonomous vehicles (AV) to improve traffic efficiency and safety, many studies have shown that traffic accidents in a hybrid traffic environment where both AV and human-driven vehicles (HVs) are present are inevitable because of the unpredictability of HVs. Given that eliminating accidents is impossible, an achievable goal is to design AVs in a way so that they will not be blamed for any accident in which they are involved in. In this paper, we propose <bold>BlaFT Rules</bold> – or <bold>Bla</bold>me-<bold>F</bold>ree hybrid <bold>T</bold>raffic motion planning <bold>Rules</bold>. An AV following <bold>BlaFT Rules</bold> is designed to be cooperative with HVs as well as other AVs, and will not be blamed for accidents in a structured road environment. We provide proofs that no accidents will happen if all AVs are using a <bold>BlaFT Rules</bold> conforming motion planner, and that an AV using <bold>BlaFT Rules</bold> will be blame-free even if it is involved in a collision in hybrid traffic. We implemented a motion planning algorithm that conforms to <bold>BlaFT Rules</bold> called <bold>BlaFT</bold>. We instantiated scores of <bold>BlaFT</bold> controlled AVs and HVs in an urban roadscape loop in the SUMO simulator and show that over time that as the percentage of <bold>BlaFT</bold> vehicles increases, the traffic becomes safer even with HVs involved. Adding <bold>BlaFT</bold> vehicles increases the efficiency of traffic as a whole by up to 34% over HVs alone.
Original language | English (US) |
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Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | IEEE Transactions on Intelligent Vehicles |
DOIs | |
State | Accepted/In press - 2023 |
Keywords
- Accidents
- Autonomous vehicles
- Law
- Machine learning
- Planning
- Roads
- Safety
ASJC Scopus subject areas
- Automotive Engineering
- Control and Optimization
- Artificial Intelligence