Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.

Signalized intersections play a vital role in addressing the issue of transportation efficiency and vehicle fuel economy in urban areas. Meanwhile, with the development of Connected and Automated Vehicles (CAVs), the mixed traffic environment composed of traffic participants with differing intelligent levels will become an important stage of the intelligent transportation system. Considering the changes in the mixed traffic environment, this paper proposes a Coupled Vehicle-Signal Control (CVSC) method to optimize the traffic signal timing and driving trajectories of CAVs at the same time, with the goals of traffic efficiency improvement and energy saving respectively. The signal timing is continuously optimized to minimize the total delay at the intersection. CAVs generate eco-driving trajectories using the received signal timing information and the planned arrival time to reduce fuel consumption. Finally, simulation experiments were been carried out to verify the control effect of the proposed CVSC. On the one hand, the influence of the involved parameters on the optimization results was analyzed and discussed. On the other hand, the proposed CVSC method was compared with the traditional CACC control and the classic eco-driving model, GlidePath. The research results show that the proposed CVSC method can effectively improve the performance of signalized intersections. When the penetration rate of CAVs is greater than 40%, this method can save fuel consumption by 6%-14% and increase the average speed by 1%-5%.

This paper describes an adaptive fault-tolerant triple-integration vehicle navigation system. The proposed triple-integration system is based on a tightly coupled design, which fuses measurements from the Global Navigation Satellite System (GNSS), an Inertial Navigation System (INS), and optical velocity sensors (OVS). The difference between GNSS-measured and INS-derived pseudorange and pseudorange rate, and the difference between INS-derived and OVS-measured velocity are taken as the measurement inputs to the system filter, which uses them to obtain accurate and continuous navigation solutions. However, to ensure the optimality and to improve the robustness of the integration system in GNSS-challenging environments, a fault-tolerant fault detection and processing (FDP) algorithm is then applied to detect faults and further process the detected faults in fault-varying methods. The detected faults can be classified as Outliers and Biases according to the designed fault factor; Outliers are excluded and Biases are still utilized by adaptively adjusting the corresponding measurement noise covariance matrix. To evaluate the performance of the proposed triple-integration system, a road test was conducted under different scenarios, including GNSS open-sky, GNSS partly blocked, and GNSS-difficult conditions. The results indicate that the proposed integration system can generate more accurate position solutions than a loosely coupled system. To verify the fault-tolerant ability of the proposed FDP algorithm, typical types of GNSS faults are simulated. The results confirm that the proposed FDP algorithm can improve the system performance in terms of its fault-tolerant ability and accuracy as well.

The BeiDou Navigation Satellite System (BDS) has been widely applied in many areas, including communications, transportation, emergency rescue, public security, surveying, precise positioning, and many other industrial applications. The integration of BDS and an Inertial Navigation System (INS) has the ability to achieve a more consistent and accurate positioning solution. However, during long BDS outages, the performance of such an integrated system degrades because of the characteristics of the Inertial Measurement Unit (IMU) - the sensor errors accumulate rapidly with time when operated in a standalone mode. In this paper, a BDS/INS/odometer/map-matching (MM) positioning methodology for train navigation applications is proposed, to solve the problem of positioning during BDS outages when trains pass through signal obstructed areas such as under bridges, inside tunnels and through deep valleys. The seamless transition of the train operation in various scenarios can be therefore maintained. When the train operates in an open-sky environment the BDS signals are available to provide accurate positioning, and the integrated BDS/INS/odometer/MM system is used to correct the INS errors using BDS measurements and to calculate the velocity using the odometer in the navigation frame so as to improve the system reliability and accuracy. In addition, the integrated system also delivers accurate positioning measurements at a high update rate. When the BDS signals are blocked the integrated system switches to the INS/odometer/MM mode, and the integrated system corrects the INS errors by using odometer measurements. In order to evaluate the proposed system a real experiment was conducted on the Qinghai-Tibet railway in western China. The experimental results indicate that the proposed system can provide accurate and continuous positioning results in both open-sky and BDS signal-obstructed environments.

GNSS/INS integrated navigation and positioning technology is widely used for train operations applications seeking a decrease in dependence on trackside equipment. However, during train operations, environmental factors such as bridges, tunnels, hillsides, and buildings will block the satellite signals. When INS works in a stand-alone mode, the system errors accumulate with time due to the inertial sensor characteristics. To overcome this problem, light detection and ranging (lidar) equipment can be introduced to help control INS divergence. The lidar point cloud data is first processed using the random sample consensus (RANSAC) algorithm to determine the track plane, then the moving average filtering (MAF) algorithm is used to detect and classify the rails. The frogs can be detected based on the changing rail number and the constraint condition for turnout parameters. With the assistance of a previously surveyed turnout database, the geographical positions of the detected frogs can be obtained and integrated with INS data to converge INS sensor errors when GNSS is not available. In this paper a lidar-aided GNSS/INS integrated navigation system is proposed that can ensure continuous positioning in both GNSS-available and GNSS-blocked scenarios. GNSS/INS is applied when the system operates in the GNSS-available scenario, and switches to the INS/lidar mode when the GNSS signals are blocked. To evaluate the performance of the proposed system, an actual train experiment was conducted along the Qinghai-Tibet railway. The results confirm that the proposed lidar-aided navigation system can achieve accurate and continuous positioning. When GNSS is not available, the proposed method can effectively suppress the error of INS with a DRMS of 1.66 m, which is a 70.4% improvement compared with a conventional GNSS/INS integrated system.

Connected and Autonomous Vehicle (CAV) technology provides the possibility of Slot-based Intersections. A space–time slot-based CAV operational control approach is proposed in this paper. Links connected to the intersection are divided into three areas: approach area, adjust area and maintain area. The double-loop Spatial Position Associating Time Series (SPATS) method and the Acceleration Dynamically Adjusting based on Predicted Trajectory (ADAPT) method are applied to guide the vehicles. The double-loop SPATS guarantees the vehicle safety, improves the traffic efficiency and maintains the performance of the method in heavy traffic. The ADAPT calculates the vehicle time-position curve as smoothly as possible. A simulation environment was established to evaluate the effectiveness of the approach. In the simulated intersection, the intersection can reach a maximum capacity of 5200 vehicles per hour. Other analyses, such as signal, stop sign and platoon control strategies, have demonstrated the superiority of the approach.