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Through these fundamental works, we realize that (1) the Chinese urban bus cycle has some specialties compared with foreign ones, and these specialties cause differences on the design criteria and design results of the hybrid buses; (2) the parallel configuration is better than the series one for the Chinese cycle from both fuel consumption and cost points of view.

Autonomous driving technology, as the product of the fifth stage of the information technology revolution, is of great significance for improving urban traffic and environmentally friendly sustainable development. Autonomous driving can be divided into three main modules. ...After that, based on the CARLA virtual urban driving environment, the DDPG algorithm and the PER-DDPG algorithm with different reward functions were simulated and verified, and tested in different scenarios.

Roundabouts are one of the most complex traffic environments in urban roads, and a key challenge for intelligent driving decision-making. Deep reinforcement learning, as an emerging solution for intelligent driving decisions, has the advantage of avoiding complex algorithm design and sustainable iteration. ...This research provides an effective approach for applying reinforcement learning algorithms to complex urban autonomous driving decisions.

There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements.

With the increasing number of urban cars, parking has become the primary problem that people face in daily life. Therefore, many scholars have studied the automatic parking system.

Design a simulation test plan for commonly used B-class road surfaces and a common speed of 60 km/h under urban driving conditions. To demonstrate the ADE-PID control effect of the ADE algorithm tuning, the ADE algorithm is compared with manually tuned PID control and passive suspension under the same simulation conditions.

Autonomous driving in real-world urban traffic must cope with dynamic environments. This presents a challenging decision-making problem, e.g. deciding when to perform an overtaking maneuver or how to safely merge into traffic.

This paper describes a novel recognition and classification method of vehicle targets in urban road based on a vehicle-mounted Velodyne HDL64E light detection and ranging (LIDAR) system.