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

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities

A Rule Based Control Algorithm for on-Ramp Merge With Connected and Automated Vehicles

One of the designs for future highways with the flow of Connected Automated Vehicles (CAVs) cars will be a dedicated lane for the CAVs to form platoons and travel with higher speeds and lower headways. The connectivity will enable the formation of platoons of CAVs traveling beside non-platoon lanes. The advent of connectivity between vehicles and the infrastructure will enable advanced control strategies ̶ improving the performance of the traffic ̶ to be incorporated in the traffic system. The merge area in a multilane highway with CAVs is one of the sections which can be enhanced by the operation of a control system. In this research, a model is developed for investigating the effects of a Rule Based control strategy yielding a more efficient and systematic method for the vehicles joining the highway mainlines comprised of platoon and non-platoon lanes. The actions tested for assisting the merge process included deceleration in the mainlines and lane change to join a platoon in the platoon lane. The model directs every CAV entering a multi-lane highway from an on-ramp, to the rightmost lane of the highway based on the appropriate action which is selected according to the traffic demand conditions and location of the on-ramp vehicle. To account for car following behavior, the vehicles in the platoon lanes are assumed to have a simplified CACC (cooperative adaptive cruise control) and those in the non-platoon lanes the IDM+ car-following model. The IDM+ car following model is modified with additional controls to incorporate the current technologies of Advanced Driver Assistant Systems (ADAS). The results of this study showed that the proposed car following model can increase the throughput of the non-platoon lane from approximately 2000 vehicle per hour (vph) to 3400 vph while the platoon lanes each had an average throughput of 3500 vph. The merge model enabled higher merging throughput for the merge area compared to current day conditions and displayed the potential for improved traffic performance in a connected environment comprised of platoon and non-platoon lanes. The results of this research will help in the design and development of advanced systems for controlling on-ramp merge sections in the future with CAVs