Emergency Lane-Change Maneuvers of Autonomous Vehicles

1998 ◽  
Vol 120 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Zvi Shiller ◽  
Satish Sundar
Keyword(s):  
Autonomous Vehicles ◽  
Minimum Distance ◽  
Phase Plane ◽  
Lane Change ◽  
Initial Speed ◽  
Mass Model ◽  
Clearance Curve ◽  
Bicycle Model ◽  
Straight Line ◽  
Longitudinal Distance

This paper addresses the issue of collision avoidance using lane-change maneuvers. Of particular interest is to determine the minimum distance beyond which an obstacle cannot be avoided at a given initial speed. Using a planar bicycle model, we first compute the sharpest dynamically feasible maneuver by minimizing the longitudinal distance of a lane transition, assuming given initial and free final speeds. The minimum distance to an obstacle is then determined from the path traced by the optimal maneuver. Plotting the minimum distance in the phase plane establishes the clearance curve, a valuable tool for planning emergency maneuvers. For the bicycle model, the clearance curve is shown to closely correlate with the straight line produced by a point mass model. Examples demonstrate the use of the clearance curve for planning safe avoidance maneuvers.

scholarly journals Lane change maneuver for autonomous vehicles (Benchmark Proposal)

10.29007/5hxt ◽  
2018 ◽  
Author(s):  
Nikolaos Kekatos ◽  
Daniel Heß ◽  
Goran Frehse
Keyword(s):  
Autonomous Vehicles ◽  
Lane Change ◽  
Lane Changes ◽  
Cooperative Driving ◽  
Bicycle Model ◽  
Moving Vehicles ◽  
Control Scheme ◽  
Lateral Behavior ◽  
The Right ◽  
Left Lane

Lane changes are known to be risky maneuvers both for autonomous vehicles and human drivers since they require changes in longitudinal and lateral velocities in the presence of other moving vehicles. In this paper, we propose a benchmark modeling a cooperative lane change maneuver that involves four fully autonomous vehicles; three in the left lane and one in the right. The vehicle driving in the right lane aims to move to the left lane while avoiding a collision with the other vehicles. Each vehicle is equipped with sensors and can also communicate with its neighboring vehicles. The vehicle dynamics are described by a dynamic bicycle model and each vehicle is equipped with a linear low-level controller that regulates its own longitudinal and lateral behavior. To guarantee that the maneuver is safe and the traffic rules are enforced, we employ a cooperative driving control scheme (in the spirit of supervisory logic) that decides the actions of each vehicle.

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

Lane change decision planning for autonomous vehicles

2020 ◽  
Author(s):  
Kangqiang Ouyang ◽  
Yong Wang ◽  
Yanqiang Li ◽  
Yunhai Zhu
Keyword(s):  
Autonomous Vehicles ◽  
Lane Change

scholarly journals Lane-deviation penalty formulation and analysis for autonomous vehicle avoidance maneuvers

2021 ◽  
pp. 095440702110079
Author(s):  
Pavel Anistratov ◽  
Björn Olofsson ◽  
Lars Nielsen
Keyword(s):  
Objective Function ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Emergency Situation ◽  
Change Scenario ◽  
Initial Speed ◽  
Objective Functions ◽  
Double Lane Change ◽  
Tire Friction

Autonomous vehicles hold promise for increased vehicle and traffic safety, and there are several developments in the field where one example is an avoidance maneuver. There it is dangerous for the vehicle to be in the opposing lane, but it is safe to drive in the original lane again after the obstacle. To capture this basic observation, a lane-deviation penalty (LDP) objective function is devised. Based on this objective function, a formulation is developed utilizing optimal all-wheel braking and steering at the limit of road–tire friction. This method is evaluated for a double lane-change scenario by computing the resulting behavior for several interesting cases, where parameters of the emergency situation such as the initial speed of the vehicle and the size and placement of the obstacle are varied, and it performs well. A comparison with maneuvers obtained by minimum-time and other lateral-penalty objective functions shows that the use of the considered penalty function decreases the time that the vehicle spends in the opposing lane.

Minimum Safety Distances for Emergency Braking Maneuvers in Car-Following Applications

2020 ◽  
Author(s):  
Devin Schafer ◽  
Pingen Chen
Keyword(s):  
Aerodynamic Force ◽  
Lane Change ◽  
Passenger Vehicle ◽  
Lane Changing ◽  
Car Following ◽  
Emergency Braking ◽  
Safety Distance ◽  
Bicycle Model ◽  
Vehicle Efficiency ◽  
Different Types

Abstract Platooning/car following has been considered as a promising approach for improving vehicle efficiency due to the reduction of aerodynamic force when closely following a pilot vehicle. However, safety is a major concern in the close car platooning/following. This paper investigates the minimum inter-vehicle distances required for a passenger vehicle to safely travel behind a heavy-duty truck with three different types of emergency maneuvers. The three emergency maneuvers considered are braking only, steering only, and braking then steering, where steering refers to a single lane change maneuver. Numerical analysis is conducted for deriving the clearance space in the braking only scenario. In addition, simulations are conducted in MATLAB/Simulink, using a bicycle model for the vehicle dynamics, to examine the minimum safe following distance for the other two scenarios. The simulation results show that, for initial vehicle speeds greater than 8 m/s, a lane change maneuver requires the shortest safety distance. Braking followed by lane changing usually requires the largest minimum safety distance.

scholarly journals Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

2018 ◽  
Vol 233 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Armin Norouzi ◽  
Milad Masoumi ◽  
Ali Barari ◽  
Saina Farrokhpour Sani
Keyword(s):  
Optimization Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Lateral Control ◽  
Lane Changing ◽  
Dynamic Constraints ◽  
Bicycle Model ◽  
Double Lane Change ◽  
Backstepping Controller

In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

scholarly journals End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5443
Author(s):  
Hongyu Hu ◽  
Ziyang Lu ◽  
Qi Wang ◽  
Chengyuan Zheng
Keyword(s):  
Autonomous Vehicles ◽  
The State ◽  
Steering Wheel ◽  
Lane Change ◽  
Simulation Environment ◽  
Longitudinal Control ◽  
Driving Style ◽  
Lane Changing ◽  
Policy Gradient ◽  
End To End

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

2019 ◽  
Vol 2673 (8) ◽  
pp. 298-309 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Alan Karr ◽  
Nagui M. Rouphail ◽  
Gyounghoon Chun ◽  
Shams Tanvir
Keyword(s):  
Autonomous Vehicles ◽  
Lateral Displacement ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Lane Changes ◽  
Detection Systems ◽  
Level Information

With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning.

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