Semantic Traffic Law Adaptive Decision-Making for Self-Driving Vehicles

Facts proved that obeying traffic laws keeps the promise to promote the safety of self-driving vehicles. Current self-driving vehicles usually have fixed algorithms during autonomous driving, however the traffic laws may differ or change in different regions or times, e.g., tidal lanes. It raises a crucial requirement to make self-driving vehicles adapt to the newly received traffic laws. The challenges are that traffic laws are usually semantic and manually designed, but the original algorithms may not always contain the pre-designed interface to adapt to emerging laws. To this end, this work proposes a traffic law adaptive decision-making platform, which uses the linear temporal logic (LTL) formula to consistently describe the semantic traffic laws. Then, an LTL-based reinforcement learning framework is designed to estimate the probability of illegal behavior under different traffic laws. Finally, a law-specific backup policy is designed to maintain the performance threshold by monitoring the probability of illegal behavior. This work takes three typical scenarios where the traffic laws differ for instance to prove the effectiveness of the proposed approach, i.e., law amendment presented by the government, law difference between different regions, and temporary traffic control. The results show that the proposed method can help the original decision-making algorithms adapt to the traffic laws well without pre-defined interfaces. This method provides a way to administer on-road driving self-driving vehicles.