Traffic light optimization with low penetration rate vehicle trajectory data.

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world.

Dense reinforcement learning for safety validation of autonomous vehicles.

One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critical events1. Here we report the development of an intelligent testing environment, where artificial-intelligence-based background agents are trained to validate the safety performances of autonomous vehicles in an accelerated mode, without loss of unbiasedness. From naturalistic driving data, the background agents learn what adversarial manoeuvre to execute through a dense deep-reinforcement-learning (D2RL) approach, in which Markov decision processes are edited by removing non-safety-critical states and reconnecting critical ones so that the information in the training data is densified. D2RL enables neural networks to learn from densified information with safety-critical events and achieves tasks that are intractable for traditional deep-reinforcement-learning approaches. We demonstrate the effectiveness of our approach by testing a highly automated vehicle in both highway and urban test tracks with an augmented-reality environment, combining simulated background vehicles with physical road infrastructure and a real autonomous test vehicle. Our results show that the D2RL-trained agents can accelerate the evaluation process by multiple orders of magnitude (103 to 105 times faster). In addition, D2RL will enable accelerated testing and training with other safety-critical autonomous systems.

Safety assessment of highly automated driving systems in test tracks: A new framework.

Among the three major safety assessment methods (i.e., simulation, test track, and on-road test) for highly automated driving systems (ADS), test tracks provide high fidelity and a safe and controllable testing environment. However, due to the lack of realistic background traffic, scenarios that can be tested in test tracks are usually static and limited. To address this limitation, a new safety assessment framework is proposed in this paper, which integrates an augmented reality (AR) testing platform and a testing scenario library generation (TSLG) method. The AR testing platform generates simulated background traffic in test tracks, which interact with subject ADS under test, to create a realistic traffic environment. The TSLG method can systematically generate a set of critical scenarios under each operational design domain (ODD) and the critical scenarios generated from the TSLG method can be imported into the AR testing platform. The proposed framework has been implemented in the Mcity test track at the University of Michigan with a Level 4 ADS. Field test results show that the proposed framework can accurately and efficiently evaluate the safety performance of highly ADS in a cost-effective fashion. In the cut-in case study, the proposed framework is estimated to accelerate the assessment process by 9.87×104 times comparing to the on-road test approach.