A Human-like Model to Understand Surrounding Vehicles Lane Changing Intentions for Autonomous Driving

In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.

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Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147626 ◽

2020 ◽

Author(s):

Dong Chen ◽

Longsheng Jiang ◽

Yue Wang ◽

Zhaojian Li

Keyword(s):

Reinforcement Learning ◽

Decision Model ◽

Autonomous Driving ◽

Lane Changing

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Reliability-Based Assessment of Potential Risk for Lane-Changing Maneuvers

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211010800 ◽

2021 ◽

pp. 036119812110108

Author(s):

Yang-Jun Joo ◽

Ho-Chul Park ◽

Seung-Young Kho ◽

Dong-Kyu Kim

Keyword(s):

Safety Evaluation ◽

Autonomous Driving ◽

Lane Change ◽

Lane Changing ◽

Time To Collision ◽

Lane Changes ◽

Structural Time Series ◽

Reliable Assessment ◽

Reliability Method ◽

Predicted Values

Despite the urgent need for continuous risk assessments during autonomous driving, achieving reliable assessment results is still challenging because of the unpredictable behaviors of adjacent human drivers and the resulting complexity. Such complexity increases particularly during lane changes because several vehicles need to interact with other vehicles. Therefore, this paper proposes a new framework to analyze lane-changing risk on freeways considering the forecastability in adjacent vehicles. Virtual lane-change scenarios are constructed based on historical maneuvers in adjacent vehicles, and the risk of potential lane change is evaluated through the safety evaluation result of the scenario. Adjacent vehicles’ future maneuvers are predicted using a multivariate Bayesian structural time series model, and the forecastability is estimated as the standard error of the predicted values. The failure probability of those lane-changing scenarios is obtained through the first-order reliability method, assuming that failure occurred when any time-to-collision value for adjacent vehicles was less than a threshold at the end of the lane change. This study tested two scenarios with three levels of uncertainty to show the effect of uncertainty on the level of risk. The results showed that the reduced uncertainty allowed a clearer distinction between risky situations. The proposed framework differentiates itself from existing methods by estimating higher risk in an adjacent vehicle’s more significant uncertainties. It is expected that the outcome of this study will be valuable in developing reliable lane-change strategies in autonomous driving.

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Social Force Control for Human-Like Autonomous Driving

Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education ◽

10.1115/detc2018-86288 ◽

2018 ◽

Author(s):

DoHyun Daniel Yoon ◽

Beshah Ayalew

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Social Force ◽

Cruise Control ◽

Lane Changing ◽

Safe Navigation ◽

Proposed Model ◽

The Social ◽

Efficient Integration

An autonomous driving control system that incorporates notions from human-like social driving could facilitate an efficient integration of hybrid traffic where fully autonomous vehicles (AVs) and human operated vehicles (HOVs) are expected to coexist. This paper aims to develop such an autonomous vehicle control model using the social-force concepts, which was originally formulated for modeling the motion of pedestrians in crowds. In this paper, the social force concept is adapted to vehicular traffic where constituent navigation forces are defined as a target force, object forces, and lane forces. Then, nonlinear model predictive control (NMPC) scheme is formulated to mimic the predictive planning behavior of social human drivers where they are considered to optimize the total social force they perceive. The performance of the proposed social force-based autonomous driving control scheme is demonstrated via simulations of an ego-vehicle in multi-lane road scenarios. From adaptive cruise control (ACC) to smooth lane-changing behaviors, the proposed model provided a flexible yet efficient driving control enabling a safe navigation in various situations while maintaining reasonable vehicle dynamics.

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A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5473 ◽

2020 ◽

Vol 34 (01) ◽

pp. 1202-1209

Author(s):

Yifan Zhang ◽

Jinghuai Zhang ◽

Jindi Zhang ◽

Jianping Wang ◽

Kejie Lu ◽

...

Keyword(s):

Motion Planning ◽

High Efficiency ◽

Computing Time ◽

Sampling Strategy ◽

Autonomous Driving ◽

Sampling Strategies ◽

Lane Changing ◽

Learning Framework ◽

Sample Points ◽

Labeling Scheme

Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers.

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Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change

Applied Sciences ◽

10.3390/app10051626 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1626 ◽

Cited By ~ 1

Author(s):

Xiaodong Wu ◽

Bangjun Qiao ◽

Chengrui Su

Keyword(s):

Trajectory Planning ◽

Autonomous Vehicle ◽

Safety Margin ◽

Autonomous Driving ◽

Simulation System ◽

Lane Change ◽

Lane Changing ◽

Lane Changes ◽

Gray Prediction ◽

Safety Margins

A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system.

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