Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing

TL;DR

This paper introduces an efficient algorithm for autonomous vehicles to predict trajectories more accurately and detect abnormal human driving behaviors in hybrid traffic, enhancing safety without compromising privacy.

Contribution

It presents the first algorithm that fuses shared run-time information for improved trajectory prediction and real-time detection of abnormal human driving behaviors with formal guarantees.

Findings

Outperforms baseline trajectory prediction methods on NGSIM and Argoverse datasets.

Achieves 97.3% detection rate with 1.2s delay and zero false alarms in simulations.

Demonstrates effectiveness in both highway and urban traffic scenarios.

Abstract

Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes, putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, etc. On the other hand, modern vehicle-to-vehicle communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other. In this paper, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers privacy. To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of 97.3%, average detection delay of 1.2s, and 0 false alarm.