A Probabilistic Approach to Driver Assistance for Delay Reduction at Congested Highway Lane Drops
Goodarz Mehr, Azim Eskandarian
备注：
Manuscript accepted for publication in the International Journal of Transportation Science and Technology. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
This paper proposes an onboard advance warning system based on a probabilistic prediction model that advises vehicles on when to change lanes for an upcoming lane drop. Using several traffic- and driver-related parameters such as the distribution of inter-vehicle headway distances, the prediction model calculates the likelihood of utilizing one or multiple lane changes to successfully reach a target position on the road. When approaching a lane drop, the onboard system projects current vehicle conditions into the future and uses the model to continuously estimate the success probability of changing lanes before reaching the lane-end, and advises the driver or autonomous vehicle to start a lane changing maneuver when that probability drops below a certain threshold. In a simulation case study, the proposed system was used on a segment of the I-81 interstate highway with two lane drops - transitioning from four lanes to two lanes - to advise vehicles on avoiding the lane drops. The results indicate that the proposed system can reduce average delay by up to 50% and maximum delay by up to 33%, depending on traffic flow and the ratio of vehicles equipped with the advance warning system.
We consider the scenario where human-driven/autonomous vehicles with low/high occupancy are sharing a segment of highway and autonomous vehicles are capable of increasing the traffic throughput by preserving a shorter headway than human-driven vehicles. We propose a toll lane framework where a lane on the highway is reserved freely for autonomous vehicles with high occupancy, which have the greatest capability to increase social mobility, and the other three classes of vehicles can choose to use the toll lane with a toll or use the other regular lanes freely. All vehicles are assumed to be onl…
Beijing Institute of Technology（北京理工大学）；University of California at Berkeley（加州大学伯克利分校）；McGill University（麦吉尔大学）
Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring and exploiting knowledge of the interaction process from demonstrations by humans. However, it is unclear what information (or environmental states) is utilized by the human driver to guide their behavior throughout the whole merging process. This paper provides quantitative analysis and evaluation of the merging behavior at highway on-ramps with congested traffic in a volume of time and space. Two types of social i…
This paper presents an on-board advance warning system for vehicles based on a probabilistic prediction model that advises them on when to change lanes to reach a highway diverge on time. The system is based on a model that estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several traffic-related parameters such as the distribution of inter-vehicle headway distances as well as driver-related parameters like lane change duration. For an upcoming diverge, the advance warning system uses the model to continuously calculate…
We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users. The dynamics of road users are modelled using Markov jump systems, in which the switching variable describes the desired lane of the vehicle under consideration and the continuous state describes the pose and velocity of the vehicles. We assume the switching probabilities of the underlying Markov chain to be unknown. As the vehicle is observed and thus, samples from the Markov chain are dr…
Vehicles on highway on-ramps are one of the leading contributors to congestion. In this paper, we propose a prediction framework that predicts the longitudinal trajectories and lane changes (LCs) of vehicles on highway on-ramps and tapers. Specifically, our framework adopts a combination of prediction models that inputs a 4 seconds duration of a trajectory to output a forecast of the longitudinal trajectories and LCs up to 15 seconds ahead. Training and Validation based on next generation simulation (NGSIM) data show that the prediction power of the developed model and its accuracy outperforms…
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究所）；Virginia Tech Transportation Institute（弗吉尼亚理工大学交通研究所）
University of Maryland, College Park（马里兰大学帕克分校）；Honda Research Institute（本田研究院）
This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules. Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic. We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort. We further incorporate various modifications in the proposed approach to improve the overall co…
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric) and that highway throughput is improved by optimally selecting the cooperatin…
Boston University（波士顿大学）；Honda Research Institute-US (HRI-US)（美国本田研究院（HRI-US））
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" only seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric). Additionally, when performing lane-changing maneuvers, we optimally sel…
Lane-free vehicle movement has been recently proposed for connected automated vehicles (CAV) due to various potential advantages. One such advantage stems from the fact that incremental changes of the road width in lane-free traffic lead to corresponding incremental changes of the traffic flow capacity. Based on this property, the concept of internal boundary control was recently introduced to flexibly share the total road width and capacity among the two traffic directions of a highway in real-time, in response to the prevailing traffic conditions, so as to maximize the cross-road (both direc…
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem, where the AVs (on both merge lane and through lane) collaboratively learn a policy to adapt to HDVs to maximize the traffic throughput. We develop an efficient and scalable MARL framework that can be used in dynamic traffic where the communication topology could be time-varying. Parameter sharing and local rewards …
Institute of Power Machinery and Vehicular Engineering, Faculty of Engineering, Zhejiang University（浙江大学工学部动力机械及车辆工程研究所）
Highway pilot assist has become the front line of competition in advanced driver assistance systems. The increasing requirements on safety and user acceptance are calling for personalization in the development process of such systems. Inspired by a finding on drivers' car-following preferences on lateral direction, a personalized highway pilot assist algorithm is proposed, which consists of an Intelligent Driver Model (IDM) based speed control model and a novel lane-keeping model considering the leading vehicle's lateral movement. A simulated driving experiment is conducted to analyse driver g…
ITU Artificial Intelligence and Data Science Research Center and Department of Aeronautical Engineering, Istanbul Technical University（伊斯坦布尔技术大学人工智能与数据科学研究中心和航空工程系）；Eatron Technologies；Faculty of Mechatronics Engineering, Istanbul Technical University（伊斯坦布尔技术大学机电一体化工程学院）；Department of Computer Engineering, University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校计算机工程系）；Faculty of Aeronautics and Astronautics, Aerospace Engineering, Istanbul Technical University（伊斯坦布尔技术大学航空航天学院航空航天工程系）；Istanbul Technical University（伊斯坦布尔技术大学）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Eatron Technologies, Istanbul, Turkey（土耳其伊斯坦布尔Eatron Technologies）；ITU Artificial Intelligence and Data Science Research Center（伊斯坦布尔技术大学人工智能与数据科学研究中心）；Department of Aeronautical Engineering, Istanbul Technical University（伊斯坦布尔技术大学航空工程系）
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.
To develop driving automation technologies for human, a human-centered methodology should be adopted for ensured safety and satisfactory user experience. Automated lane change decision in dense highway traffic is challenging, especially when considering the personalized preferences of different drivers. To fulfill human driver centered decision algorithm development, we carry out driver-in-the-loop experiments on a 6-Degree-of-Freedom driving simulator. Based on the analysis of the lane change data by drivers of three specific styles,personalization indicators are selected to describe the driv…
A new motion planning framework for automated highway merging is presented in this paper. To plan the merge and predict the motion of the neighboring vehicle, the ego automated vehicle solves a joint optimization of both vehicle costs over a receding horizon. The non-convex nature of feasible regions and lane discipline is handled by introducing integer decision variables resulting in a mixed integer quadratic programming (MIQP) formulation of the model predictive control (MPC) problem. Furthermore, the ego uses an inverse optimal control approach to impute the weights of neighboring vehicle c…
A recently proposed paradigm for vehicular traffic in the era of CAV (connected and automated vehicles), called TrafficFluid, involves lane-free vehicle movement. Lane-free traffic implies that incremental road widening (narrowing) leads to corresponding incremental increase (decrease) of capacity; and this opens the way for consideration of real-time internal boundary control on highways and arterials, in order to flexibly share the total (both directions) road width and capacity among the two directions in dependence of the bi-directional demand and traffic conditions, so as to maximize the …
This article examines a symbolic numerical approach to optimize a vehicle's track for autonomous driving and collision avoidance. The new approach uses the classical cost function definition incorporating the essential aspects of the dynamic state of the vehicle as position, orientation, time sampling, and constraints on slip angles of tires. The optimization processes minimize the cost function and simultaneously determine the optimal track by varying steering and breaking amplitudes. The current velocity of the vehicle is limited to a maximal velocity, thus, allowing a stable search of the o…
In recent years, automotive technology has made a steady progress. In particular, Advanced Driver Assistance System (ADAS) has enabled many safety features in commercial vehicles, for instance, pedestrian detection, lane keeping assist, emergency automatic braking, etc. Although these features provide drivers with a safer operational environment, crashes still happen occasionally due to the complex road conditions and the unpredictable movement of road users including vehicles, pedestrians, bicyclists, and non-motorized vehicles. In this paper, we aim at predicting the possibilities of crashes…
In preparing for connected and autonomous vehicles (CAVs), a worrisome aspect is the transition era which will be characterized by mixed traffic (where CAVs and human-driven vehicles (HDVs) share the roadway). Consistent with expectations that CAVs will improve road safety, on-road CAVs may adopt rather conservative control policies, and this will likely cause HDVs to unduly exploit CAV conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV. Without cooperation from other vehicles in the traffic stream, it can be extremely unsafe for th…
A risk-averse preview-based $Q$-learning planner is presented for navigation of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is represented by a finite-state non-stationary Markov decision process (MDP). A risk assessment unit module is then presented that leverages the preview information provided by sensors along with a stochastic reachability module to assign reward values to the MDP states and update them as scenarios develop. A sampling-based risk-averse preview-based Q-learning algorithm is finally developed that generates samples using the preview information…
Motivated by the potentially high downlink traffic demands of commuters in future autonomous vehicles, we study a network architecture where vehicles use Vehicle-to-Vehicle (V2V) links to form relay network clusters, which in turn use Vehicle-to-Infrastructure (V2I) links to connect to one or more Road Side Units (RSUs). Such cluster-based multihoming offers improved performance, e.g., in coverage and per user shared rate, but depends on the penetration of V2V-V2I capable vehicles and possible blockage, by legacy vehicles, of line of sight based V2V links, such as those based on millimeter-wav…
Vehicle localization in highway tunnels is a challenging issue for autonomous vehicle navigation. Since GPS signals from satellites cannot be received inside a highway tunnel, map-aided localization is essential. However, the environment around the tunnel is composed mostly of an elliptical wall. Thereby, the unique feature points for map matching are few unlike the case outdoors. As a result, it is a very difficult condition to perform vehicle navigation in the tunnel with existing map-aided localization. In this paper, we propose tunnel facility-based precise vehicle localization in highway …
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed. The classic graph search and sampling methods first perform path planning and then configure the corresponding speed, which lacks a strategy to deal with the high-speed obstacles. Decoupling optimization methods perform motion planning in the S-L and S-T domains respectively. These methods require a large free configuration space to plan the lane change trajectory. In dense dynamic scenes, it is easy to cause the failure of trajectory planning an…
A quantitative understanding of dynamic lane-changing (LC) interaction patterns is indispensable for improving the decision-making of autonomous vehicles, especially in mixed traffic with human-driven vehicles. This paper develops a novel framework combining the hidden Markov model and graph structure to identify the difference in dynamic interaction networks between mandatory lane changes (MLC) and discretionary lane changes (DLC). A hidden Markov model is developed to decompose LC interactions into homogenous segments and reveal the temporal properties of these segments. Then, conditional mu…
Highway on-ramps are regarded as typical bottlenecks in transportation networks. In previous work, mainline vehicles' selfish lane choice behavior at on-ramps is studied and regarded as one cause leading to on-ramp inefficiency. When on-ramp vehicles plan to merge into the mainline of the highway, mainline vehicles choose to either stay steadfast on the current lane or bypass the merging area by switching to a neighboring lane farther from the on-ramp. Selfish vehicles make the decisions to minimize their own travel delay, which compromises the efficiency of the whole on-ramp. Results in previ…
This paper develops and investigates the impacts of multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) in a microscopic traffic simulation environment. Eco-routing is a technique that finds the most energy efficient route. ICEV and BEV energy consumption patterns are significantly different with regard to their sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared to highway trips. Different energy consumption p…
Traditional traffic optimization solutions assume that the graph structure of road networks is static, missing opportunities for further traffic flow optimization. We are interested in optimizing traffic flows as a new type of graph-based problem, where the graph structure of a road network can adapt to traffic conditions in real time. In particular, we focus on the dynamic configuration of traffic-lane directions, which can help balance the usage of traffic lanes in opposite directions. The rise of connected autonomous vehicles offers an opportunity to apply this type of dynamic traffic optim…
