With the common use of lidar sensors on intelligent vehicles, the simulation of lidar in autonomous driving is necessary. This study proposes an innovative lidar modeling method, and introduces solutions for the simulation of the lidar detection function and its physical mechanism. The model consists of geometric and physical models, and can simulate both point clouds and targets. The geometric model addresses the spatial relationship between lidar and the environment. The physical model describes how the lidar detection mechanism may influence detection results. Signal attenuation and unwanted raw data caused by raindrops are the main consideration in the physical model. Characteristics of lidar signal attenuation in different weather conditions are modeled, and a simplified lidar equation is derived for use by lidar users, as opposed to designers. Unwanted raw data are simulated in a stochastic model employing the Monte Carlo method, where raindrop size and distance are sampled. The model is calibrated and validated with real lidar data. The application of the proposed lidar model for an autonomous emergency braking system is introduced.

Poly(lactic acid) (PLA) is an emerging plastic that has insufficient properties (e.g., it is too brittle) for widespread commercial use. Previous research results have shown that the strength and toughness of basalt fiber reinforced PLA composites (PLA/BF) still need to be improved. To address this limitation, this study aimed to obtain an effective compatibilizer for PLA/BF. Melt-blending of poly(butylene adipate-co-terephthalate) (PBAT) with PLA in the presence of 4,4′-methylene diphenyl diisocyanate (MDI: 0.5 wt% of the total resin) afforded PLA/PBAT-MDI triblock copolymers. The triblock copolymers were melt-blended to improve the interfacial adhesion of PLA/BF and thus obtain excellent performance of the PLA-ternary polymers. This work presents the first investigation on the effects of PLA/PBAT-MDI triblock copolymers as compatibilizers for PLA/BF blends. The resultant mechanics, the morphology, interface, crystallinity, and thermal stability of the PLA-bio polymers were comprehensively examined via standard characterization techniques. The crystallinity of the PLA-ternary polymers was as high as 43.6%, 1.44× that of PLA/BF, and 163.5% higher than that of pure PLA. The stored energy of the PLA-ternary polymers reached 20,306.2 MPa, 5.5× than that of PLA/BF, and 18.6× of pure PLA. Moreover, the fatigue life of the PLA-ternary polymers was substantially improved, 5.85× than that of PLA/PBAT-MDI triblock copolymers. Thus, the PLA/PBAT-MDI triblock copolymers are compatibilizers that improve the mechanical properties of PLA/BF.

LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.

Minimum energy consumption with maximum comfort driving experience defines the ideal human mobility. Recent technological advances in most highly automated driving systems on electric vehicles with regenerative braking system not only enhance the safety and comfort level but also present a significant opportunity for automated eco-driving. This research focuses on the longitudinal eco-driving considering the coordinated control for 4WD intelligent electric vehicles. The intelligent electric vehicle framework with 4-wheel hub motors is established and the intention-aware longitudinal automated driving strategy for overall traffic situation levels is proposed. Further, the coordinated control strategy arbitrates the control mode basing on the traffic situation level and distributes braking forces between the electronic hydraulic braking system and the cooperative regenerative auxiliary braking system. The proposed strategy is verified in the co-simulation environment and field test respectively. Test results show optimal control effects in overall traffic situation levels and an enhanced energy recycling efficiency.

The effects of cetane number (CN) coupled with pilot injection ratio and pilot injection timing on the combustion and emission characteristics of a four-cylinder diesel engine were investigated. The results showed that as the pilot injection ratio increases, the peak cylinder gas pressure (CGP) increases, and the peak of heat release rate (HRR) increases accordingly; the coupled CN increases, the brake specific fuel consumption (BSFC) decreases, and the brake thermal efficiency (BTE) increases; CO and HC emissions increase; and NO _x emissions increase. In addition, the number concentration and total mass of particulate matter (PM) decrease with the increasing pilot injection ratio. Only when the pilot injection ratio reaches 20% does the fuel produce pilot injection heat release. The ignition delay time (ID) and combustion duration (CD) are redefined for the pilot injection heat release operating conditions. With the delay of the pilot injection timing, the peak CGP increases, the coupled CN increases, the BTE increases, the BSFC decreases, CO and HC emissions increase, and NO _x emissions decrease. With the delay of the pilot injection timing, the number concentration and total mass of PM decrease. In addition, no pilot injection heat release was generated for any of the five fuels at pilot injection timings from 30°CA BTDC to 45°CA BTDC.