Driving Etiquette

Technical ReportEstablish driving etiquette based on naturalistic driving behavior of human drivers to serve as the basisfor the design of autonomous vehicles to drive "like safe human drivers." This project queried a largeamount of naturalistic driving data from the Ann Arbor connected vehicle deployment. The data wereused to train algorithms to learn about "what is appropriate" based on statistical analysis of humandriving behaviors.United States Department of Transportationhttps://deepblue.lib.umich.edu/bitstream/2027.42/156052/4/Driving Etiquette.pdfDescription of Driving Etiquette.pdf : Final Repor

Coordination and Analysis of Connected and Autonomous Vehicles in Freeway On-Ramp Merging Areas

Freeway on-ramps are typical bottlenecks in the freeway network, where the merging maneuvers of ramp vehicles impose frequent disturbances on the traffic flow and cause negative impacts on traffic safety and efficiency. The emerging Connected and Autonomous Vehicles (CAVs) hold the potential for regulating the behaviors of each individual vehicle and are expected to substantially improve the traffic operation at freeway on-ramps. The aim of this research is to explore the possibilities of optimally facilitating freeway on-ramp merging operation through the coordination of CAVs, and to discuss the impacts of CAVs on the traffic performance at on-ramp merging.In view of the existing research efforts and gaps in the field of CAV on-ramp merging operation, a novel CAV merging coordination strategy is proposed by creating large gaps on the main road and directing the ramp vehicles into the created gaps in the form of platoon. The combination of gap creation and platoon merging jointly facilitates the mainline and ramp traffic and targets at the optimal performance at the traffic flow level. The coordination consists of three components: (1) mainline vehicles proactively decelerate to create large merging gaps; (2) ramp vehicles form platoons before entering the main road; (3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is analytically formulated as an optimization problem, incorporating the macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions.The impacts of CAV coordination strategies on traffic efficiency are investigated through illustrative case studies conducted on microscopic traffic simulation platforms. The results show substantial improvements in merging efficiency, throughput, and traffic flow stability. In addition, the safety benefits of CAVs in the absence of specially designed cooperation strategies are investigated to reveal the CAV’s ability to eliminate critical human factors in the ramp merging process

Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing

Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs' safety performance unbiasedly, ideally, the probability distributions of the joint state space of all vehicles in the simulated naturalistic driving environment (NDE) needs to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without consideration of distributional consistency of driving behaviors, which may cause significant evaluation biasedness for AV testing. To fill this research gap, a distributionally consistent NDE modeling framework is proposed. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions, which serve as the basic behavior models. To reduce the model errors caused by the limited data quantity and mitigate the error accumulation problem during the simulation, an optimization framework is designed to further enhance the basic models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. In the case study of highway driving environment using real-world naturalistic driving data, the distributional accuracy of the generated NDE is validated. The generated NDE is further utilized to test the safety performance of an AV model to validate its effectiveness.Comment: 32 pages, 9 figure

Proactive Safety Measure Using Road Environment Assessment Program (REAP)

In Malaysia, reactive crash statistics have becoming very crucial in evaluating road’s safety level and in deciding crash-prone areas known as black spot. The establishments of these statistics normally take years to complete as a result of several well-known setbacks within developing countries. Those obstacles had produced poor crash database having low accessibility, reliability and adequateness of crash data that finally brought major impact to the entire road safety system.  In light of that, a proactive safety measure called Road Environment Assessment Program (REAP) has been developed to help evaluating the environment risk factors of a road, calculating the risk index and presented the results through Google Earth platform. REAP was developed based on composite risk index value aggregated from 14 road environment indicators existed in most Malaysia federal roads. Based on the local conditions of the selected roads, specific road environment risk factor were produced where trend and risk level as well as identifications of riskiest road section could be easily identified. REAP is a time-saving and cost-saving tool as it can directly recognize problematic road environment factors while planning on the best and suitable road improvement procedures for problematic sections