Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Using Combined Lane Change and Variable Speed Limit Control Techniques Can Ease Congestion and Reduce Fuel Use and Emissions

Traffic during peak hours is getting worse over time and the duration of the peak is increasing in most metropolitan areas as more drivers try to use limited roadway capacity. Bottlenecks caused by traffic incidents or road construction limit roadway capacity even further and can cause traffic “shock waves.” When an incident causes a highway lane to close unexpectedly, vehicles are forced to change lanes close to the incident and at low speeds. These forced lane changes interfere with traffic flow in open lanes and decrease the overall flow of the roadway. Heavy-duty trucks can exacerbate congestion because they are larger and slower than passenger vehicles. Advanced technologies may help to improve traffic flow in these situations. Variable speed limits can change based on road, traffic, and weather conditions. Speed limits can be reduced in real time when congestion is imminent to smooth traffic flow and handle more traffic volume at a slower, but not stop-and-go, speed. Lane change control systems provide lane change recommendations well upstream of blocked lanes, spreading lane changes over a greater distance and minimizing bottlenecks that disrupt traffic flow.This policy brief summarizes findings from researchers at the University of Southern California who simulated traffic patterns along a section of Interstate 710 near the Ports of Long Beach/Los Angeles, a congested area that gets substantial truck traffic. They simulated the use of variable speed limit and lane change control systems to evaluate the potential traffic impacts of these systems.This brief is based on research from two NCST projects: Eco-Friendly Intelligent Transportation System Technology for Freight Vehicles, and Reducing Truck Emissions and Improving Truck Fuel Economy via ITS Technologies

A formulation of the relaxation phenomenon for lane changing dynamics in an arbitrary car following model

Lane changing dynamics are an important part of traffic microsimulation and are vital for modeling weaving sections and merge bottlenecks. However, there is often much more emphasis placed on car following and gap acceptance models, whereas lane changing dynamics such as tactical, cooperation, and relaxation models receive comparatively little attention. This paper develops a general relaxation model which can be applied to an arbitrary parametric or nonparametric microsimulation model. The relaxation model modifies car following dynamics after a lane change, when vehicles can be far from equilibrium. Relaxation prevents car following models from reacting too strongly to the changes in space headway caused by lane changing, leading to more accurate and realistic simulated trajectories. We also show that relaxation is necessary for correctly simulating traffic breakdown with realistic values of capacity drop

Highway Discretionary Lane-change Decision and Control Using Model Predictive Control

To enable autonomous vehicles to perform discretionary lane change amidst the random traffic flow on highways, this paper introduces a decision-making and control method for vehicle lane change based on Model Predictive Control (MPC). This approach divides the driving control of vehicles on highways into two parts: lane-change decision and lane-change control, both of which are solved using the MPC method. In the lanechange decision module, the minimum driving costs for each lane are computed and compared by solving the MPC problem to make lane-change decisions. In the lane-change control module, a dynamic bicycle model is incorporated, and a multi-objective cost function is designed to obtain the optimal control inputs for the lane-change process. Additionally, A long-short term memory (LSTM) model is used to predict the trajectories of surrounding vehicles for both the MPC decision and control modules. The proposed lane-change decision and control method is simulated and validated in a driving simulator under random highway traffic conditions

Discrete choice modelling for traffic densities with lane-change behaviour

This paper investigates the modelling for traffic densities with lane-change behaviour using the information provided by loop detectors. The existing studies on traffic density estimation for multi-lane roadways mainly focus on the scenario where either vehicles’ lane-change manoeuvres are not common or the lane-change pattern is time-invariant. This research, however, takes into consideration the time-varying nature of drivers’ lane-change manoeuvres, and models the lane-change probabilities using a number of discrete choice models. These lane-change models are then embedded into a state space model to capture the dynamics of traffic flow. The extended Kalman filter is used to update the estimated traffic densities of multi-lane motorways. A numerical study is carried out to investigate the performance of the developed approach

Winning strategies in congested traffic

One-directional traffic on two-lanes is modeled in the framework of a spring-block type model. A fraction $q$ of the cars are allowed to change lanes, following simple dynamical rules, while the other cars keep their initial lane. The advance of cars, starting from equivalent positions and following the two driving strategies is studied and compared. As a function of the parameter $q$ the winning probability and the average gain in the advancement for the lane-changing strategy is computed. An interesting phase-transition like behavior is revealed and conclusions are drawn regarding the conditions when the lane changing strategy is the better option for the drivers.Comment: 5 pages, 5 figure

Hybrid stochastic kinetic description of two-dimensional traffic dynamics

In this work we present a two-dimensional kinetic traffic model which takes into account speed changes both when vehicles interact along the road lanes and when they change lane. Assuming that lane changes are less frequent than interactions along the same lane and considering that their mathematical description can be done up to some uncertainty in the model parameters, we derive a hybrid stochastic Fokker-Planck-Boltzmann equation in the quasi-invariant interaction limit. By means of suitable numerical methods, precisely structure preserving and direct Monte Carlo schemes, we use this equation to compute theoretical speed-density diagrams of traffic both along and across the lanes, including estimates of the data dispersion, and validate them against real data